Neuroadaptive XR Interfaces: Brain-Driven Control in Augmented, Virtual, and Mixed Reality Ecosystems

Executive Summary

Neuroadaptive Extended Reality (XR) interfaces represent a paradigm shift in human-computer interaction, enabling direct brain-driven control within immersive digital environments. This innovative field integrates Brain-Computer Interface (BCI) technology with Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) ecosystems, promising to revolutionize how individuals interact with digital content and the physical world. By dynamically adapting to a user’s real-time cognitive and emotional states, neuroadaptive XR aims to deliver unprecedented levels of immersion, personalization, and accessibility across diverse sectors, including gaming, healthcare, and professional training.

The foundational role of BCI in this evolution is critical, translating neural activity into actionable commands without physical intervention. This report delves into the various BCI modalities, from invasive implants offering high signal fidelity to non-invasive EEG and fNIRS systems that prioritize user comfort and portability. It meticulously outlines the complex signal processing pipeline, from raw brainwave acquisition to sophisticated AI-powered decoding, which underpins seamless brain-driven control.

While the transformative potential is immense, particularly in enhancing user experience and democratizing access for individuals with disabilities, significant technical, user experience, and ethical challenges persist. These include issues of signal quality, latency, calibration, potential cognitive overload, and profound concerns regarding mental privacy, data security, and the imperative for responsible AI governance. Overcoming these hurdles necessitates a concerted, interdisciplinary effort, fostering innovation rooted in human-centered design principles and robust ethical frameworks to ensure that neuroadaptive XR evolves as a beneficial and equitable technology.

 

1. Introduction to Neuroadaptive XR Interfaces

 

The landscape of human-computer interaction is undergoing a profound transformation, driven by the convergence of immersive technologies and advanced neurosensing capabilities. At the forefront of this evolution are neuroadaptive Extended Reality (XR) interfaces, systems designed to dynamically respond to a user’s internal brain states, thereby enabling a more intuitive and integrated digital experience. This section establishes a foundational understanding of XR modalities and the core principles of neuroadaptive technology, culminating in an exploration of how Brain-Computer Interfaces (BCI) are becoming the essential conduit for this brain-driven control.

 

1.1 Defining Extended Reality (XR): Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR)

 

Extended Reality (XR) functions as an overarching term, encompassing a spectrum of immersive technologies that seamlessly blend digital and physical elements to create a continuum of experiences.1 This rapidly expanding field is poised for substantial market growth, with projections indicating an increase from $20 billion in 2025 to $123 billion by 2032. Such a trajectory positions XR as the “4th major disruptive technology wave,” following the advent of personal computers, the Internet, and smartphones, signaling its impending ubiquity in daily life.2

Within the XR umbrella, three distinct modalities define varying degrees of immersion and interaction with the physical world:

• Virtual Reality (VR) completely immerses users within fully simulated digital environments, effectively replacing their perception of the real world.1 Users don a VR headset to enter a simulated reality that engages multiple senses, transporting them to fantastical realms, realistic simulations, or dedicated virtual workspaces.4 This technology is exemplified by 360-degree videos, interactive 3D gaming simulations, and virtual meeting rooms that replicate physical workspaces to foster familiarity and collaboration.3
• Augmented Reality (AR), in contrast, overlays digital elements onto the real world, thereby enhancing the user’s perception and creating a composite view that combines both physical and digital aspects.1 Users remain grounded in their actual physical environment, with virtual graphics, text, video, or audio superimposed directly onto their view.3 While AR overlays typically do not interact with the real world, user engagement with these digital elements can be enabled through specific software.3 Practical applications include visualizing furniture within a living space before purchase or receiving real-time navigation instructions overlaid onto a street view.3
• Mixed Reality (MR) represents a more advanced and interactive form of AR, characterized by its seamless blending of digital and physical elements. Crucially, MR allows these virtual and real components to coexist and interact with each other in real-time.1 This capability enables users to manipulate and engage with both physical and digital objects simultaneously within a hybrid environment.4 MR demonstrates significant potential in fields such as education and training, where immersive simulations provide hands-on experience in realistic scenarios without the inherent risks of the physical world. For instance, medical students can practice complex surgical procedures in a safe and controlled MR environment, while engineers can visualize and interact with intricate machinery designs prior to constructing physical prototypes.3

The progression across AR, MR, and VR is not merely a categorical distinction but rather a continuum of immersion and interaction, each offering unique capabilities for blending digital content with human experience. The substantial market growth projected for XR underscores an impending mainstream adoption of these technologies. For this widespread integration to be truly successful and user-friendly, the current friction often associated with manual controls and traditional input methods must be overcome.6 This implies that more intuitive, brain-driven control mechanisms are not merely an enhancement but a necessary evolutionary step. Such advancements are essential to unlock XR’s full potential, enabling a truly seamless and integrated experience that moves beyond the limitations of conventional human-computer interaction.

 

1.2 Understanding Neuroadaptive Interfaces: Real-time Adaptation to User Brain States

 

Neuroadaptive interfaces represent a cutting-edge approach to human-machine system design, characterized by their ability to dynamically alter functional characteristics of computer-based displays and controls in response to meaningful variations in a user’s cognitive and/or emotional states.7 These systems are engineered to promote safer and more effective human-machine performance by establishing a more symmetrical form of communication between users and computer-based systems than currently exists.8

The underlying mechanism of neuroadaptive technology operates within a closed control loop, continuously utilizing real-time measures of neurophysiological activity to enable intelligent software adaptation.9 This involves detecting subtle shifts in a user’s brain activity—such as changes indicative of attention levels, stress, cognitive workload, or emotional states—through the use of specialized tools like EEG headsets or other biosensors. Once these neural data are collected, the interface or experience is adapted accordingly, moving beyond passive monitoring to actively shaping the user’s interaction.6

The benefits derived from these adaptive interfaces are multifaceted. They offer real-time personalization, allowing systems to respond instantly to a user’s evolving cognitive state. This capability can significantly reduce friction, particularly in high-stakes tasks where cognitive load needs careful management. Furthermore, neuroadaptive interfaces enhance accessibility for users with various impairments and establish adaptive feedback loops that create more empathetic and responsive digital environments.11 For example, a productivity application could simplify its menu when it detects signs of mental fatigue in the user, or a meditation application could increase soothing visuals in response to heightened stress levels. Similarly, if a user appears confused, the system might modify task flows or offer contextual help prompts.11

The fundamental value proposition of neuroadaptive interfaces stems from their capacity to address a critical communications disconnect inherent in traditional human-computer interaction. In conventional systems, computers lack direct, independent awareness of a user’s internal state, leading to a one-way communication paradigm where the user must explicitly command the machine. By enabling symmetrical communications, neuroadaptive technology facilitates a shift from explicit, direct control to implicit adaptation. This means the system no longer solely waits for overt commands but actively senses and responds to the user’s internal cognitive and emotional states. This proactive adaptation directly mitigates the friction and cognitive burden caused by manual configuration and traditional input methods, leading to a more natural, intuitive, and less demanding interaction. The ability of the system to understand and respond to the user’s mental state in real-time is crucial for creating truly seamless and integrated experiences in the evolving XR landscape.

 

1.3 The Convergence: Brain-Computer Interfaces (BCI) as the Foundation for Neuroadaptive XR

 

The realization of neuroadaptive XR interfaces is fundamentally reliant on the advancements in Brain-Computer Interface (BCI) technology. A BCI, often referred to as a Brain-Machine Interface (BMI), establishes a direct communication link between the brain’s electrical activity and an external device, effectively bypassing the need for physical movement.12 The primary objectives of BCI research and development include mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions.12

BCI technology is undergoing rapid evolution, poised to revolutionize not only the Internet of Things (IoT) but, more significantly, the entire Extended Reality (XR) ecosystem.13 This technology enables direct brain-to-device communication, allowing for the control or interaction with devices without any physical intervention from the user.13 The integration of BCI with XR systems means that third-party software embedded within Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) environments can access sensors that read brain activity, thereby providing additional communication channels and substantially increasing the bandwidth of human-AR/VR interaction.13

The purpose of this convergence is to unlock a new dimension of interactive experiences. In AR, this fusion enables hands-free manipulation of information for medical professionals, direct neural command of robotic devices, and intuitive control over smart environments.15 Within VR systems, BCI connectivity facilitates the creation of deeply immersive artificial environments where users can interact with virtual worlds through thought alone.15 This synergy is instrumental in fostering new forms of entertainment, art, and gaming, offering profoundly immersive and personalized experiences. Furthermore, it provides innovative tools for cognitive training, emotion regulation, and advanced information management systems.16 The strategic importance of this convergence is underscored by significant investments from major industry players, such as Meta, who are heavily committing resources to the development of the metaverse and integrated BCI technologies.13

The integration of BCI with XR represents a fundamental transformation in human-computer interaction, shifting the user’s role from an external operator to an intrinsic component of the digital environment. This direct communication pathway bypasses traditional input methods like keyboards, mice, or handheld controllers, leading to a more natural and seamless interaction. This seamlessness is a crucial prerequisite for truly neuroadaptive experiences, where the system can intelligently anticipate and respond to the user’s internal state. The ability to control and interact with XR environments directly through neural activity enhances the sense of presence and reduces cognitive load associated with conventional interfaces. The substantial investment by major industry players like Meta further validates the perceived strategic importance of this convergence as the future of immersive computing. This progression implies that BCI is not just an add-on but a core enabling technology that will drive the next generation of intuitive and integrated digital experiences.

 

Table 1: Comparison of Extended Reality (XR) Modalities

 

The following table provides a concise overview of the distinct characteristics of Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR), highlighting their varying degrees of immersion and interaction with the physical world, all encompassed under the umbrella term of Extended Reality (XR).

 

Modality	Definition	Immersion Level	Interaction with Real World	Typical Hardware	Key Use Cases
Augmented Reality (AR)	Overlays digital elements onto the real world, enhancing perception and creating a composite view.	Low (real world enhanced)	Yes (overlays digital content onto physical environment)	Smartphones, tablets, smart glasses 3	Navigation, virtual try-ons, product information visualization 3
Mixed Reality (MR)	Seamlessly blends digital and physical elements, allowing them to coexist and interact in real-time.	Medium (seamless blend)	Yes (co-exists and reacts with physical objects)	MR headsets (e.g., Meta Quest Pro, Microsoft HoloLens) 3	Surgical training, engineering design, airport management, product demonstrations 3
Virtual Reality (VR)	Creates a fully immersive digital environment, replacing the real world entirely.	High (replaces real world)	No (physical surroundings are blocked out)	VR headsets (e.g., Meta Quest, HTC Vive) 3	Gaming, virtual meetings, simulations, therapy, immersive storytelling 3
Extended Reality (XR)	An umbrella term encompassing AR, VR, and MR, describing any technology that alters reality by adding digital elements.	Continuum (from low to high)	Varies (from overlays to full replacement)	Diverse range of devices, often head-mounted displays 1	Broad applications across industries, including healthcare, education, manufacturing, entertainment 2

 

2. Brain-Computer Interface (BCI) Fundamentals for XR Control

 

Brain-Computer Interfaces (BCIs) are the core technology enabling brain-driven control in Extended Reality (XR) ecosystems. These systems translate neural activity into actionable commands, fundamentally altering how humans interact with digital environments. Understanding the various BCI modalities and their intricate signal processing pipelines is crucial for appreciating their potential and current limitations in neuroadaptive XR.

 

2.1 BCI Modalities: Invasive, Semi-Invasive, and Non-Invasive Approaches

 

BCI systems acquire brain signals through diverse methods, broadly categorized by their level of invasiveness. The chosen modality directly impacts signal quality, cost, and the spectrum of practical applications.17

• Invasive BCIs: These methods entail the surgical implantation of electrodes directly into or onto the brain.17 Such direct placement yields superior signal quality, characterized by high temporal and spatial resolution, providing precise information on the timing and localization of neural activity patterns.18 However, their deployment is constrained by high costs, complex surgical procedures, and inherent post-surgical risks.18 Consequently, invasive BCIs are predominantly confined to specialized research settings or critical medical applications, particularly for patients with severe motor impairments where no other communication means exist.18 Notable examples include the micro-electrode arrays utilized in the BrainGate project, which decode neural signals associated with movement intent to operate external devices, and other cortical implants.20
• Semi-Invasive BCIs: These approaches involve positioning electrodes within the skull but external to the brain tissue itself, with Electrocorticography (ECoG) being a prime example.17 This modality offers a balance between signal quality and invasiveness, providing better resolution compared to non-invasive methods while mitigating some of the higher risks associated with full brain surgery.19
• Non-Invasive BCIs: These methods do not necessitate any surgical procedure, relying instead on external sensors to record brain activity.17

• Electroencephalography (EEG): EEG stands as the most widely adopted non-invasive BCI technology due to its cost-effectiveness, portability, and capacity to capture brain activity in real-time.18 It functions by measuring the electric potentials generated by neuronal firing on the scalp.18 Despite these advantages, EEG signals are frequently characterized by inherent noise, instability, and lower spatial resolution, which complicates the reliable decoding of complex neural activity.22 Nevertheless, ongoing research is actively addressing these limitations through advancements in material designs for stable, high-fidelity EEG acquisition and the development of sophisticated machine learning tools to enhance decoding performance.22 The increasing availability of consumer-grade EEG headsets further underscores its growing relevance.24
• Functional Near-Infrared Spectroscopy (fNIRS): fNIRS measures hemodynamic signals, specifically detecting changes in blood oxygenation, by utilizing near-infrared light.23 While offering benefits such as portability and reduced susceptibility to noise, its slower temporal response limits its utility in real-time applications, particularly those requiring rapid brain-driven control.22
• Other Non-Invasive Methods: Techniques such as Magnetoencephalography (MEG), functional Magnetic Resonance Imaging (fMRI), and Positron Emission Tomography (PET) also measure brain activity. However, their substantial size, high cost, and limited portability generally restrict their practical daily use in BCI applications, often confining them to specialized laboratory or clinical environments.22

A critical trade-off exists in BCI development: the pursuit of superior signal quality and precision often comes at the cost of increased invasiveness and expense. While invasive methods undeniably provide the most granular and accurate neural data, the strong drive towards non-invasive, wearable solutions is paramount for the widespread adoption of neuroadaptive XR in consumer and everyday applications. This push for accessibility means that the future of neuroadaptive XR is not solely dependent on achieving perfect signal fidelity through invasive means. Instead, it relies heavily on significant advancements in signal processing and artificial intelligence (AI) to effectively compensate for the inherent noise, lower spatial resolution, and variability of non-invasive brain signals in dynamic XR environments. The ability to extract meaningful information from these more accessible signals is key to unlocking the full potential of brain-driven control for a broader user base.

 

2.2 The BCI Signal Processing Pipeline: From Acquisition to Command Generation

 

A Brain-Computer Interface (BCI) system operates through a sophisticated, multi-stage pipeline that meticulously interprets raw brain signals, extracts meaningful features, decodes underlying patterns, and ultimately translates them into actionable commands for external devices.18 This intricate process is fundamental to enabling seamless brain-driven control within XR environments.

The pipeline typically commences with:

• Signal Acquisition: This initial step is the bedrock of any BCI system, where brain activity is measured.18 For non-invasive BCIs, this most commonly involves placing electrodes on the scalp to record Electroencephalography (EEG) signals.18 The overall performance of the entire BCI system is critically dependent on the quality and signal-to-noise ratio (SNR) of these initially acquired brain signals.28 If the raw data is poor, subsequent processing stages will struggle to yield accurate results.
• Preprocessing and Noise Reduction: Raw brain signals are inherently noisy and frequently contaminated with various artifacts originating from sources such as muscle movements, eye blinks, or external electrical interference.18 The preprocessing stage is indispensable for cleaning these unwanted components. This involves applying a range of techniques, including band-pass filtering to isolate relevant frequency ranges, low-pass filtering to remove high-frequency noise, specific artifact removal algorithms, baseline correction to normalize signal amplitudes, and downsampling to reduce data volume while preserving essential information.23 The primary objective at this stage is to maximize the signal-to-noise ratio, ensuring that only the most relevant brain activity is isolated for subsequent analysis.18
• Feature Extraction: Following thorough preprocessing, the high-dimensional, cleaned brain signal data must be simplified into a more concise and manageable “feature vector”.18 This crucial step involves analyzing the signal to extract specific patterns or characteristics that are most relevant for accurate translation into control commands.23 Common techniques employed at this stage include time-frequency analysis, which examines how signal power is distributed across different frequencies over time; spatial filtering methods, such as Common Spatial Patterns (CSP), which identify optimal spatial filters to enhance discriminative features; and spectral power analysis, which quantifies the power spectral density of the signal in specific frequency bands.30
• Classification and Decoding: Once the feature vectors are computed from a set of training data, a classifier or decoder is trained. This training process teaches the algorithm to detect specific brain states that correspond to desired control commands.18 This stage is pivotal in determining the type of mental task the user is performing or their intended command.29 A wide array of classification techniques are utilized, ranging from simpler linear methods like Linear Discriminant Analysis (LDA) to highly complex deep neural networks, which can learn intricate patterns from large datasets.18 Artificial intelligence (AI) and machine learning (ML) play a transformative role here, significantly enhancing the accuracy and robustness of this decoding process by identifying subtle neural patterns that might otherwise be missed.18
• Control Interface: The final component of the pipeline involves transmitting the classified command signal to an external device or application.18 This enables the user to interact with and control various elements within the XR environment, such as manipulating cursors on a screen, operating robotic arms, or navigating complex virtual spaces, thereby achieving the intended brain-driven interaction.18

The BCI signal processing pipeline is a highly interdependent, multi-stage process where the efficacy of later stages, particularly feature extraction and classification, is fundamentally constrained by the quality of initial signal acquisition and the thoroughness of preprocessing. This inherent dependency means that even the most advanced AI/ML algorithms for decoding will struggle to perform optimally if the raw brain signals are excessively noisy or contain significant artifacts. This highlights that achieving robust and reliable BCI performance in dynamic XR environments requires not only continuous advancements in AI/ML algorithms but also significant breakthroughs in sensor technology and real-time noise reduction techniques. Addressing the “noisy, unstable, and low spatial resolution” nature of non-invasive brain signals at the earliest possible stage is crucial for ensuring the overall accuracy and responsiveness of neuroadaptive XR systems. Therefore, optimizing BCI for XR is a holistic challenge, demanding integrated improvements across the entire pipeline, from the physical sensors and their seamless integration into XR headsets to the sophisticated algorithms that interpret the data, rather than focusing on any single component in isolation.

 

2.3 Key Brain Signals for XR Interaction

 

Brain-Computer Interfaces (BCIs) leverage various types of brain signals to enable control and interaction within Extended Reality (XR) environments. These signals can be broadly categorized into those used for explicit, volitional control and those for implicit, passive monitoring of a user’s cognitive and emotional states.

• Motor Imagery (MI-BCI): This paradigm relies on the user imagining a specific movement, such as moving a limb, without actually performing the physical action. This mental exercise elicits measurable changes in sensorimotor rhythms (SMR) within the brain, detectable via Electroencephalography (EEG).18 These EEG-detectable changes are then translated into commands to interact with an XR environment, allowing for hands-free control.33 MI-BCI is a common and effective approach in neurorehabilitation, enabling patients to control virtual avatar arms or receive sensory feedback, thereby promoting motor recovery and neuroplastic changes.14 For instance, VR-based neurofeedback training has significantly enhanced motor imagery performance, leading to improved classification accuracy and reduced task duration, proving particularly beneficial for stroke rehabilitation.34
• P300 Event-Related Potentials: The P300 is a distinctive positive-going event-related potential (ERP) that appears in the EEG approximately 300 milliseconds after a user recognizes a rare or significant target stimulus.18 It is typically elicited through an “oddball paradigm,” where a target stimulus is presented infrequently among non-target stimuli.18 P300-based BCIs are advantageous for their relatively short training times, high accuracy, and their capacity to support a larger number of distinct commands, making them suitable for more complex control interfaces compared to some other BCI types.31 A practical application includes a drone control system that utilizes P300-based BCI, allowing users to control virtual or real drones by focusing on blinking directional buttons. This system has demonstrated consistent performance and user experience across both VR and AR environments.31
• Steady-State Visually Evoked Potentials (SSVEP): SSVEP refers to a co-frequency and harmonic response observed in the EEG that is elicited by visual stimuli flickering at specific frequencies.36 SSVEP-BCIs are characterized by high information transfer rates (ITR) and signal-to-noise ratios (SNR), often requiring minimal user training or calibration, making them relatively easy to deploy.36 SSVEP-BCI is a promising candidate for integration into consumer electronics devices.37 Current research is exploring the use of 3D stereoscopic stimuli within VR environments to optimize SSVEP-BCI for applications in three-dimensional spaces, further enhancing immersive control.37
• Passive BCI for Implicit State Monitoring: Passive BCIs are specifically designed to unobtrusively decipher aspects of a user’s mental state in real-time from brain activity recordings, such as EEG.38 This capability allows a computer system to dynamically adapt its behavior to enhance the subjective user experience without requiring explicit volitional control from the user.38 This is a crucial distinction, as it enables a more fluid and less cognitively demanding interaction. For instance, neuroadaptive haptics systems leverage reinforcement learning from brain-decoded neural signals (implicit feedback) to dynamically adjust XR feedback, which effectively reduces cognitive load and enhances user immersion.6 Studies also demonstrate the utility of adapting interfaces based on real-time cognitive workload measurements to improve learning efficiency in training scenarios, dynamically adjusting the pace of information presentation to the learner’s cognitive state.42

The evolution of BCI in XR signifies a strategic shift from explicit, command-based control towards implicit, passive monitoring of cognitive and emotional states. While explicit control methods (such as Motor Imagery, P300, and SSVEP) offer direct user agency by translating conscious intentions into commands, passive BCI enables a more seamless, intuitive, and less cognitively demanding interaction. This is achieved by the system anticipating user needs and adapting the XR environment accordingly, without requiring overt user input. This implicit adaptation is crucial for reducing user fatigue, especially during prolonged XR experiences, and for significantly enhancing the sense of immersion and presence. By allowing the system to subtly adjust to the user’s mental state, the interaction becomes more natural and fluid, moving towards a truly symbiotic human-computer interaction where the digital environment feels like a natural extension of the user’s mind rather than a tool requiring constant conscious effort. This deeper level of integration is essential for the widespread adoption and long-term acceptance of XR technologies.

 

3. Applications of Neuroadaptive XR Interfaces

 

The integration of neuroadaptive capabilities into Extended Reality (XR) ecosystems is unlocking transformative applications across a multitude of sectors. By leveraging brain-driven control and real-time adaptation to user states, these interfaces are poised to redefine engagement, enhance human performance, and improve quality of life.

 

3.1 Gaming and Entertainment

 

Neuroadaptive XR is fundamentally reshaping the gaming and entertainment industries by allowing players to control games and interactive narratives directly with their brainwaves, thereby merging neuroscience with interactive entertainment.24 This goes beyond traditional input methods, enabling a more intuitive and immersive experience.

Early examples of neurogaming demonstrated the potential of brainwave control for simple interactions. Mindflex, for instance, allowed players to manipulate a ball through an obstacle course using only their concentration and relaxation levels.24 Similarly, “Throw Trucks With Your Mind!” enabled players to control virtual objects and trigger in-game actions based on their mental focus and relaxation.24 More recently, NeuroRacer, a cognitive training tool, showed how neurogaming could improve mental agility and attention in older adults by requiring players to navigate a car while simultaneously responding to various signs and signals.24

The integration of Virtual Reality (VR) has opened new dimensions of interactivity and immersion in neurogaming. VR neurogames, such as Neurable’s “Awakening,” allow players to interact with virtual environments using their brainwaves, enabling actions like picking up objects or typing numbers with thought alone.24 This technology provides a “computer mouse for the mind,” offering a novel way to select items in a virtual world.45

Beyond direct control, neuroadaptive gaming is evolving to dynamically adjust gameplay based on a player’s real-time brain state. This includes systems that adapt difficulty to maximize player engagement, preventing boredom when a game is too easy or frustration when it is too challenging.40 By monitoring brain activity related to engagement, systems can subtly alter game features, behaviors, and scenarios in real-time, aiming to maintain the player in an optimal “Flow” state.40 This can involve adjusting enemy spawning rates, modifying time limits, or even changing mission scenarios based on player performance and mental state.50

In entertainment beyond traditional gaming, neuroadaptive interfaces are enabling adaptive narratives. For example, a film can be designed with multiple parallel channels of footage, where the viewer’s attention levels or meditation state, as recorded by an EEG device, can dynamically influence which footage is displayed.51 This creates a personalized and partially involuntary control over the narrative, aiming to increase narrative immersion without requiring conscious effort from the user.51 The broader entertainment sector is also leveraging XR for immersive experiences, from virtual art galleries and cinematic experiences that transport viewers into movies with ultra-high-resolution displays and spatial audio, to live sports broadcasts offering impossible camera angles and real-time stats in a virtual courtside seat.52

The application of neuroadaptive technology in gaming and entertainment signifies a profound shift beyond traditional input methods, moving towards a more intuitive and empathetic interaction between the user and the digital world. This progression enables experiences that are not only more deeply immersive but also highly personalized, dynamically adjusting to the user’s mental and emotional state. By continuously monitoring brain activity, these systems can anticipate user needs and optimize the experience in real-time, leading to heightened engagement, reduced frustration, and a stronger sense of presence within virtual environments. This capability is crucial for creating truly compelling and adaptive digital entertainment, where the experience is tailored to the individual, fostering a deeper connection and enjoyment.

 

3.2 Healthcare and Rehabilitation

 

Neuroadaptive XR interfaces are poised to revolutionize healthcare and rehabilitation by offering personalized therapy plans, increasing patient engagement through immersive environments, and providing real-time feedback to improve recovery outcomes.53 This technology offers a significant departure from traditional, often monotonous, therapeutic approaches.

In neurological rehabilitation, XR systems are designed to incorporate critical aspects of neuroscience and motor learning to assist patients, particularly stroke survivors, with motor recovery.54 These systems can improve shoulder and elbow function, grip and release strength, and overall functional reaching, often integrating with upper extremity robotics.55 Research indicates that VR systems can trigger cortical activation, promoting neuroplastic changes and functional improvement following a stroke.34 For example, VR-based therapy encourages patients to engage more fully with exercises, leading to better outcomes in upper limb motor function through immersive and interactive tasks that mimic real-world movements.34 The gamified elements and immediate visual feedback within VR environments activate the brain’s reward system, reinforcing repetition and maintaining motivation, which is crucial for long-term adherence to rehabilitation routines.56

For pain and anxiety management, neuroadaptive XR offers innovative solutions. The systems can be used for chronic and acute pain, providing distraction, facilitating controlled breathing, and promoting decompression and anxiety alleviation.55 Physicians and researchers are exploring VR as a safe and sustainable alternative to opioids for pain management, as VR can influence patients’ emotional states, divert attention from pain, and help block pain signals from reaching the brain.54 Neurofeedback training within immersive VR environments has also shown promise in reducing stress, improving attention, and strengthening emotional regulation by allowing participants to learn to positively influence their brain activity in real-time.57

In the realm of cognitive function improvement, neuroadaptive XR addresses areas such as attention, visual-spatial awareness, and listening skills.55 VR environments can be adapted based on real-time brain activity to enhance users’ attention, with studies showing significant reductions in alpha band power and improved task performance in neurofeedback groups compared to control groups.58

Beyond therapy, XR is transforming medical education and surgical support. Medical students can repeatedly practice techniques in life-like virtual environments, dynamically removing layers of tissue and organ systems without risk to patients.54 This simulates the movements and reactions of living patients, mitigating errors and promoting superior health outcomes.54 For surgeons, XR helps visualize organs, tumors, X-rays, and ultrasounds in real-time and from multiple angles, improving efficiency and reducing procedure time.54

Furthermore, AI-powered automation within these systems is streamlining administrative tasks, such as the auto-generation of SOAP notes in rehabilitation settings, reducing documentation time and increasing productivity for healthcare professionals.55

The application of neuroadaptive XR in healthcare and rehabilitation signifies a fundamental shift from generalized treatments to highly personalized interventions. By continuously monitoring a patient’s neural and physiological states, these systems can dynamically adjust therapeutic content, difficulty, and feedback in real-time. This dynamic adaptation not only enhances patient engagement and motivation—crucial for sustained recovery—but also optimizes therapeutic efficacy by tailoring the experience to the individual’s unique needs and progress. This capability promises to accelerate recovery, improve patient outcomes, and make advanced rehabilitation more accessible and effective.

 

3.3 Training and Education (Military & Industrial)

 

Neuroadaptive XR interfaces are significantly impacting training and education, particularly in high-stakes environments such as military and industrial sectors. These technologies accelerate learning, enhance workplace safety, and provide risk-free training opportunities that are difficult or impossible to replicate in the real world.54

In military applications, XR tools are reshaping training programs, empowering operators and maintainers to tackle evolving threats with precision and confidence.59 VR military training allows personnel to practice high-risk, dangerous situations without actual risk, such as medical procedures in combat zones, explosive ordnance disposal (EOD), and the operation of complex equipment and vehicles.60 Specific use cases include combat simulations, tactical mission planning, flight training, vehicle and tank operations, parachute jump training, improvised explosive device (IED) training, naval operations, and cybersecurity defense.60 VR also aids in PTSD and stress management by exposing soldiers to controlled, therapeutic simulations of combat scenarios.61 The immersive nature of VR creates a higher emotional connection, making learning more memorable and impactful.60

For industrial training, XR is utilized for troubleshooting, maintenance procedures, and assembly tasks.1 For instance, VR allows workers to practice diagnosing and resolving complex problems in a simulated environment, leading to faster and more efficient problem-solving and reduced downtime in manufacturing settings.60 It also enables remote assistance, connecting on-site technicians with remote experts through mixed reality overlays for real-time, step-by-step guidance.60

A key neuroadaptive capability in these training contexts is cognitive load adaptation. Systems can dynamically adjust the difficulty of training scenarios based on a trainee’s real-time mental workload, measured through neurophysiological signals like EEG or fNIRS.62 For example, in flight simulators, the system can adapt visual, auditory, and textual cues based on the pilot’s cognitive workload, ensuring optimal learning efficiency.43 Studies have demonstrated that adaptive training in VR environments can shorten overall training time without increasing cognitive overload or impairing knowledge retention, supporting its use in domains like manufacturing and military service where efficient skill acquisition is critical.62 This approach helps prevent both cognitive overload (when tasks are too demanding) and underload (leading to declines in vigilance due to automation).64

The benefits of utilizing XR in training are substantial, including a 90% increase in employee engagement, a 75% improvement in knowledge retention compared to traditional learning, and a 60% reduction in workplace accidents.60 These technologies allow for hands-on training in safe, controlled virtual environments, minimizing wear and tear on actual equipment and providing access to specialist assets that may otherwise be limited.60

The application of neuroadaptive XR in military and industrial training represents a significant advancement in optimizing human performance and safety by moving beyond static, one-size-fits-all curricula. By continuously monitoring and adapting to individual cognitive states and learning progress, these systems can provide truly personalized training experiences. This dynamic adjustment ensures that learners are consistently challenged at an optimal level, maximizing skill acquisition, knowledge retention, and the transfer of learned abilities to real-world, high-pressure situations. This capability is pivotal for creating more effective, efficient, and safer training programs across critical sectors.

 

4. Benefits and Advantages of Neuroadaptive XR

 

Neuroadaptive XR interfaces offer a compelling array of benefits that extend beyond the capabilities of traditional human-computer interaction. By deeply integrating brain-sensing technologies and adaptive algorithms, these systems promise to redefine user experience, personalization, and accessibility.

 

4.1 Enhanced Immersion and Presence

 

Neuroadaptive XR interfaces significantly enhance immersion and the feeling of presence within digital environments by dynamically tuning multisensory feedback to user preferences.6 Immersion, in the context of virtual reality, refers to the condition where the user loses awareness of being in an artificial world, experiencing it with all senses and interacting with the environment.5 Neuroadaptive systems actively contribute to this by integrating real-time neural and physiological data to modify haptics, visual, and auditory cues in virtual, augmented, or mixed environments.6

This real-time adaptation means that the XR system can adjust settings like brightness, field of view, haptic feedback, and spatial audio dynamically, based on implicit feedback from the user’s brain signals.6 This process is often guided by reinforcement learning, where the system learns user preferences over time without requiring explicit manual configuration, thereby minimizing interruptions that could break immersion.6

The use of multisensory stimulation, involving vision, proprioception, balance, and auditory input, activates multiple brain regions simultaneously, contributing to a more believable and engaging experience.56 Gamified elements, visual feedback, and performance scores within neuroadaptive XR applications stimulate the brain’s reward center, reinforcing repetition and encouraging sustained engagement.56 This is particularly evident in rehabilitation, where patients are more likely to complete sessions consistently due to the motivational power of immersive feedback loops, turning tedious exercises into engaging routines that feel less like treatment and more like exploration.56 Furthermore, advanced haptic renderings can combine vibrotactile feedback with electrical muscle stimulation to simulate not only touch but also the resistance and rigidity of virtual objects, tricking the brain into perceiving properties like texture and weight.6

The ability of neuroadaptive XR interfaces to dynamically tune multisensory feedback based on a user’s internal state transforms XR from a primarily visual experience into a truly embodied one. This creates a more believable and engaging virtual reality by reducing the cognitive effort required for interaction and fostering a deeper sense of “being there.” By implicitly adapting to the user’s preferences and physiological responses, the system minimizes friction and distractions, allowing the user’s brain to more fully accept the simulated environment as real. This enhanced immersion is critical for applications requiring high levels of engagement, such as realistic training simulations, therapeutic interventions, and deeply compelling entertainment.

 

4.2 Real-time Personalization and Adaptive Experiences

 

Neuroadaptive XR interfaces offer unparalleled real-time personalization and adaptive experiences by directly responding to a user’s cognitive and emotional states.11 These systems move beyond static, one-size-fits-all interfaces, evolving into dynamic, empathetic partners that optimize the user experience.

The core mechanism involves leveraging implicit neural and physiological data, collected from sensors like EEG headsets, as real-time indicators of a user’s preferences, engagement, and immersion.6 This data allows the system to autonomously learn user preferences over time, often through reinforcement learning, without requiring active user input or frequent manual adjustments.6 This approach ensures that personalization feels like a seamless and intuitive part of the experience, rather than a chore that breaks immersion.6

Practical applications demonstrate this adaptive capability across various scenarios:

• Cognitive Load Management: A productivity application can simplify its menu when it detects signs of mental fatigue, or a flight simulator can simplify interface elements if it senses pilot overload.11 This helps manage cognitive workload, ensuring users remain focused and productive without being overwhelmed.67
• Emotional Regulation: A meditation application can increase soothing visuals or auditory cues when it detects heightened stress levels in the user, creating a more responsive and supportive environment.11 Similarly, in therapeutic contexts, the virtual environment can be automatically adjusted to reduce stimulus intensity if a patient’s stress levels rise during exposure therapy.68
• Learning and Training Optimization: In educational settings, neuroadaptive interfaces can adjust the pace of information presentation in real-time based on the learner’s cognitive load, keeping them within their optimal “Zone of Proximal Development” (ZPD).42 This dynamic adaptation enhances learning efficiency and retention by tailoring content to individual abilities and progress.42 For instance, a neuroadaptive training protocol using fNIRS in a flight simulator demonstrated more efficient training and improved performance by adjusting difficulty based on mental workload.43
• Gaming and Entertainment: Gameplay difficulty can be dynamically adjusted to match a player’s skill and engagement level, preventing boredom or frustration and maintaining optimal “Flow”.40

The ability of neuroadaptive XR interfaces to dynamically adapt to a user’s internal state represents a significant evolution from static, one-size-fits-all interfaces to dynamic, empathetic systems. This capability optimizes user experience by reducing cognitive load, minimizing distractions, and fostering a more intuitive and natural interaction. By continuously learning and responding to individual needs and preferences, these systems create highly personalized digital environments that enhance engagement, improve performance, and support overall well-being, paving the way for more intelligent and responsive human-computer collaboration.

 

4.3 Improved Accessibility and Inclusivity

 

Neuroadaptive XR interfaces hold immense potential for significantly improving accessibility and fostering inclusivity for users with a wide range of disabilities. By leveraging brain-driven control and adaptive features, these technologies can overcome traditional physical and sensory barriers, democratizing access to digital environments and enhancing the quality of life for diverse user populations.

One of the most profound impacts is in overcoming physical barriers, particularly for individuals with severe motor impairments. Brain-Computer Interfaces (BCIs) enable hands-free control of external devices and XR environments, bypassing the need for traditional physical access methods like keyboards or joysticks.11 This allows individuals with conditions such as Amyotrophic Lateral Sclerosis (ALS), spinal cord injuries, or stroke to communicate, control robotic devices, or navigate virtual spaces using only their brain activity.15 For example, non-invasive BCI systems are being investigated to provide accessible communication options for children with severe motor impairments, integrating with existing Augmentative and Alternative Communication (AAC) practices.69 Wireless medical neurotechnologies are also being developed to assist in the diagnosis and management of neurological diseases, providing stable brain signal detection even during daily activities.20

Sensory augmentation is another critical area of improvement. For individuals with visual impairments, AR contact lenses can provide heads-up displays (HUDs) with crucial information or enhance contrast to make the world clearer.73 AR glasses can offer real-time subtitles and text descriptions of nearby sounds, providing auditory support for students with hearing disabilities, effectively bridging communication gaps in classrooms.73 These solutions often utilize AI technology for real-time captioning and text-to-speech functions.73

Furthermore, neuroadaptive XR can provide cognitive support and enhance learning for individuals with cognitive challenges. Interfaces can adapt for users with memory impairments by reordering actions or simplifying menus when mental fatigue is detected.11 The immersive nature of VR can make tech education more engaging for children with disabilities, helping them focus and acquire necessary digital skills.73 Virtual field trips, for instance, offer accessible and safe alternatives for students with physical disabilities who might otherwise be excluded from real-world excursions.73 Homework assistance can also be provided, with AR glasses reading text aloud for dyslexic students or transcribing handwriting into legible text.73

The ability of neuroadaptive XR interfaces to adapt to a user’s unique abilities and challenges, rather than requiring the user to adapt to a rigid interface, is transformative. This approach democratizes access to digital environments, entertainment, education, and even employment opportunities for individuals who were previously marginalized by conventional technology. By enhancing communication, mobility, and cognitive engagement, neuroadaptive XR significantly improves the quality of life and fosters greater independence for diverse user populations, promoting a more inclusive digital society.

 

5. Challenges and Limitations

 

Despite the immense promise of neuroadaptive XR interfaces, their widespread adoption and full potential are currently hindered by a complex array of technical, user experience, and ethical challenges. Addressing these limitations is paramount for the responsible and effective integration of brain-driven control into immersive ecosystems.

 

5.1 Technical Hurdles

 

The development and deployment of neuroadaptive XR interfaces face several significant technical hurdles that require ongoing research and innovation.

• Hardware Constraints: A primary challenge arises from the simultaneous use of XR headsets and BCI devices, which can introduce mutual interference, potentially leading to malfunctions or degraded performance.15 Current BCI hardware, particularly those offering higher signal fidelity, can be bulky, expensive, and lack portability, limiting their practical daily use.22 While efforts are underway to miniaturize sensors and integrate them seamlessly into XR headwear, achieving a comfortable and unobtrusive design without compromising signal quality remains a complex engineering task.72
• Signal Quality and Noise: Non-invasive BCI modalities, such as EEG, are preferred for their safety and portability but inherently suffer from noisy, unstable signals with low spatial resolution.22 These signals are susceptible to various artifacts caused by muscle movements, eye blinks, and external electromagnetic interference, which are particularly prevalent in dynamic XR environments where users are actively moving.39 Ensuring robust and reliable brain signal acquisition in real-world, uncontrolled settings is a persistent challenge, as noise can significantly degrade the accuracy of BCI classification.22
• Latency and Real-time Processing: For seamless and intuitive brain-driven control in XR, extremely low latency is critical. Any noticeable delay between a user’s mental command and the system’s response can disrupt immersion and lead to a frustrating user experience.2 Processing complex brain signals in real-time, especially when integrating multiple sensory modalities and adapting the environment dynamically, demands significant computational power and efficient algorithms to minimize processing delays.2
• Calibration and Generalization: Many BCI systems, particularly those for explicit control, require lengthy and subject-specific calibration periods to train the algorithms to accurately interpret an individual’s unique brain patterns.29 This extensive calibration is impractical for widespread consumer adoption and limits the generalizability of BCI models across different users and even across different sessions for the same user.38 Developing pre-calibrated classifiers and person-independent models that can adapt with minimal training data is a crucial area of ongoing research.22

The technical challenges facing neuroadaptive XR interfaces highlight the need to balance technological sophistication with practical usability. Overcoming these hurdles requires not only continued breakthroughs in sensor design and processing efficiency but also significant advancements in AI algorithms that can generalize across diverse users and effectively interpret noisy, low-resolution brain signals in real-time. This integrated approach is essential to move neuroadaptive XR from controlled laboratory environments to robust and reliable real-world applications.

 

5.2 User Experience Issues

 

Beyond technical feasibility, the successful integration of neuroadaptive XR interfaces hinges on addressing critical user experience (UX) issues, ensuring that these brain-driven systems are not only functional but also comfortable, intuitive, and cognitively manageable for users.

• Comfort and Ergonomics of Headsets: Current XR headsets, when combined with BCI sensors, can be bulky and heavy, leading to physical discomfort, pressure points, and heat buildup during prolonged use.74 Issues like eye strain from screens positioned close to the eyes and the inconvenience of removing and re-wearing headsets further deter mainstream adoption.78 The lack of realistic tactile feedback in most XR systems also diminishes the sense of “intimacy” and genuine interaction with virtual objects, as the brain relies on multisensory input for memory formation and a sense of “placed-ness”.78 Designing wearable sensors for comfort, robustness, and aesthetic appeal, while ensuring unobtrusive data collection, remains a significant challenge.74
• Cognitive Overload: Immersive and dynamic XR environments can present users with a barrage of visual, auditory, and sometimes tactile stimuli, leading to cognitive overload where the brain struggles to process and retain essential information.11 Excessive information, overwhelming visuals, complex interfaces, and a lack of spatial cues can cause confusion, disorientation, reduced engagement, and ultimately, frustration.67 While neuroadaptive interfaces aim to adjust complexity based on cognitive load, misinterpreting brain data could lead to ineffective or even detrimental personalization.11
• Learning Curve: Introducing new interaction paradigms, particularly brain-driven controls, can present a steep learning curve for users accustomed to traditional input methods. Non-intuitive gestures or complex mental commands require significant effort to master.82 Designing for “instant expertise” by leveraging existing human skills, while also supporting “progressive learning” for novices, is crucial to minimize frustration and encourage adoption.82 The goal is to make adaptation feel like a natural enhancement rather than a “magic trick” that requires conscious effort to understand or override.11
• Cybersickness: A common issue with immersive XR experiences is cybersickness, which can manifest as nausea, disorientation, and headaches.57 This is often caused by mismatches between visual motion in the virtual environment and the user’s physical vestibular input. While some neuroadaptive systems aim to mitigate this by adjusting the environment based on user discomfort, it remains a significant barrier for many potential users.68

The user experience issues associated with neuroadaptive XR interfaces highlight the critical need for human-centered design principles. Ensuring that these advanced systems are intuitive, comfortable, and cognitively manageable is paramount for their successful integration into daily life. This involves a delicate balance: leveraging brain signals to enhance the experience without overwhelming the user, maintaining transparency in adaptive behaviors, and prioritizing physical comfort and ergonomic design to prevent fatigue and discomfort. Ultimately, the goal is to create seamless interactions that feel natural and effortless, minimizing the learning curve and maximizing user engagement and well-being.

 

5.3 Ethical and Societal Concerns

 

The advent of neuroadaptive XR interfaces, while promising profound benefits, also introduces a complex web of ethical and societal concerns that demand careful consideration and proactive regulatory frameworks. These issues primarily revolve around the unprecedented access to and interpretation of highly sensitive neural data.

• Privacy of Neural Data: Neural data is uniquely sensitive because it can reveal a user’s most intimate processes, including thoughts, memories, mental states, emotions, behavior, personality, and even future health risks or cognitive performance.21 Crucially, these systems can expose subconscious and involuntary brain activity, revealing information individuals may not consciously recognize or wish to share.21 The increasing proliferation of non-invasive neurotechnology in consumer markets, often with essentially unregulated data collection practices, poses an urgent risk to mental privacy.21 Many companies’ privacy policies are vague, fail to explicitly mention neural data, and inconsistently provide users with options to withdraw consent, access, or delete their brain recordings.21 The possibility of “eavesdropping” on private verbal thought or re-identifying individuals from anonymized neural records raises significant alarm.21
• Security Vulnerabilities: The vast amounts of sensitive neural data processed by neuroadaptive XR systems present significant cybersecurity risks. Data breaches could expose highly personal information, leading to severe privacy violations and reputational damage.21 Furthermore, the potential for cyber attackers to intervene in BCI operations, altering commands derived from brain signals, could cause adverse effects to the user’s safety or compromise system integrity.28 Robust encryption protocols, access controls, and continuous monitoring are essential to safeguard this data.28
• Bias in AI Interpretation: AI algorithms, which are central to decoding brain signals and adapting XR environments, are heavily reliant on the quality and diversity of their training data.85 If these datasets are biased or incomplete, the AI can produce skewed results, reinforce existing prejudices, or misinterpret cognitive states, leading to unfair or ineffective adaptations.85 The “black box” nature of some AI models further complicates this, making it difficult for human experts to understand how the AI arrives at specific conclusions or to identify potential flaws in its reasoning.85
• User Agency and Mind Control: The ability of neuroadaptive systems to implicitly influence user preferences and mental associations, particularly through subtle neurofeedback mechanisms, raises concerns about the erosion of free deliberation and potential for political or commercial manipulation.21 The question arises whether users are truly consenting to such pervasive data collection and adaptive influence, especially when the mechanisms are not transparent.21 This could challenge the very nature of individual autonomy and mental integrity.21
• Accountability and Human Oversight: In complex neuroadaptive XR systems, determining liability for errors or unintended consequences becomes challenging due to the “diffusion of responsibility” across multiple human and AI agents.85 The lack of interpretability in AI models (the “black box” problem) makes it difficult to scrutinize the AI’s decision-making process, hindering accountability.85 Maintaining a “human-in-the-loop” approach with clear oversight mechanisms is crucial until robust ethical and regulatory frameworks mature.68
• Digital Divide: The high cost of advanced neuroadaptive XR hardware and the requirement for high-speed internet access could exacerbate existing inequalities, limiting access for individuals in resource-limited settings and creating a new form of digital divide.88

The ethical and societal concerns surrounding neuroadaptive XR interfaces underscore an imperative for robust ethical frameworks and comprehensive regulatory guidelines. These frameworks must prioritize mental privacy, data security, user agency, and algorithmic fairness. Without proactive measures to address these profound implications, the transformative potential of brain-driven control in XR could be undermined by issues of trust, equity, and human rights. Responsible development necessitates a multi-stakeholder approach to ensure that these technologies are deployed safely, ethically, and for the benefit of all.

 

6. Future Outlook and Recommendations

 

The trajectory of neuroadaptive XR interfaces points towards a future where human-computer interaction is profoundly integrated and intuitive. Realizing this vision, however, requires sustained innovation and a concerted effort to navigate the complex landscape of technological advancement, user experience, and ethical governance.

 

6.1 Advancements in BCI Technology

 

The future of neuroadaptive XR is intrinsically linked to continuous advancements in BCI technology. Key areas of progress include:

• Miniaturization and Improved Signal Quality of Non-Invasive Sensors: The trend is towards developing more inconspicuous, portable, and cost-effective non-invasive BCI devices.22 Research is focused on creating almost imperceptible microstructure brain sensors that can be inserted between hair follicles, offering high-fidelity signals for continuous BCI use in everyday life.72 This will mitigate the current challenges of noisy EEG signals and low spatial resolution, enabling more reliable decoding of complex neural activity.22
• Closed-Loop Systems: The current decade is expected to witness increased validation of BCIs in closed-loop systems that can continuously adapt to a user’s mental states.89 This means the system will not only interpret brain signals but also provide real-time feedback that influences the brain’s activity, creating a dynamic, self-optimizing interaction.89
• AI/Machine Learning for Enhanced Decoding: Artificial intelligence and machine learning algorithms will continue to play a pivotal role in enhancing the performance and accuracy of BCI decoders.22 This includes developing sophisticated models that operate on high-dimensional, minimally processed brain signals, enabling the reliable decoding of complex neural activity and real-time pattern recognition.22 The integration of generative AI into BCI platforms, as seen with Synchron’s initiative, further highlights this trend.91
• Biohybrid and Neuromorphic Systems: Future developments will likely involve biohybrid and neuromorphic systems that can adapt to the brain’s inherent biological processes.89 This could lead to more seamless and natural interfaces that mimic the brain’s own architecture and processing capabilities.66
• Transfer Learning for BCI Decoders: Research is exploring methods to transfer BCI decoders from expert users to naive users, allowing the latter to readily use a BCI while the system incrementally adapts its model parameters to their emerging modulations. Such transfer learning methods will improve transferability across subjects, days, and similar tasks, significantly reducing calibration time and making BCIs more practical for widespread use.22

 

6.2 Evolution of Human-Computer Interaction

 

Neuroadaptive XR is poised to fundamentally reshape human-computer interaction, driving an evolution towards more intuitive, natural, and symbiotic relationships between humans and technology.

• Shift from Explicit to Implicit Interaction: The most significant evolution will be a shift from explicit, command-based interaction to implicit, state-aware adaptation.38 Instead of users consciously issuing commands, systems will dynamically optimize usability, performance, and experience by inferring user intent and mental states from neural and physiological data.75 This will transform machines from passive tools into responsive partners that tune their behavior in real-time based on the user’s internal state.75
• More Natural Interfaces and Multimodal Inputs: The future will see interfaces that leverage more natural human behaviors, incorporating multimodal inputs beyond traditional controllers, such as eye tracking, hand gestures, and voice commands, seamlessly integrated with brain signals.93 This will enable a richer, more intuitive interaction where technology becomes more responsive to human intent and behavior.93
• Human Augmentation vs. Replacement: The narrative surrounding AI and automation is shifting from human replacement to human augmentation.93 Neuroadaptive XR will enhance human capabilities, allowing individuals to achieve more by offloading cognitive load and providing adaptive support, rather than simply automating tasks.93 This fosters more natural and productive collaboration between people and intelligent systems.93
• Seamless Integration of Physical and Digital Worlds: As MR technologies mature and BCI integration becomes more sophisticated, the boundary between the physical and digital worlds will increasingly dissolve.93 This will enable users to interact with digital content as a natural extension of their physical environment, creating truly immersive and integrated experiences.93

 

6.3 Recommendations for Responsible Development

 

To ensure that the transformative potential of neuroadaptive XR is realized responsibly and ethically, several key recommendations are critical:

• Prioritize Human-Centered Design: Development must focus on user comfort, minimizing cognitive load, and ensuring intuitive interaction.11 This includes designing ergonomic hardware, simplifying interfaces, and supporting progressive learning to make the technology accessible and enjoyable for a broad user base.74 Neuroadaptive features should enhance, not hijack, user goals, and users should always have the option to override adaptive behaviors.11
• Develop Robust Ethical and Legal Frameworks: Given the highly sensitive nature of neural data, comprehensive ethical and legal guidelines are urgently needed.19 These frameworks must address mental privacy, data security, informed consent, and accountability for AI-driven decisions.21 Specific attention should be paid to preventing the “eavesdropping” on private thoughts, mitigating algorithmic bias, and ensuring user agency over their neural data.21
• Foster Interdisciplinary Research and Collaboration: The complexity of neuroadaptive XR necessitates a collaborative approach involving neuroscientists, AI researchers, UX designers, ethicists, legal experts, and policymakers.15 Open dialogue and shared understanding across these disciplines are crucial for identifying potential risks, developing responsible solutions, and establishing best practices.75
• Invest in Scalable and Accessible Technologies: Efforts should be directed towards making neuroadaptive XR technologies more affordable and widely accessible to prevent the exacerbation of existing digital divides.2 This includes continued investment in non-invasive BCI research, open-source platforms, and cloud-based solutions that reduce prohibitive costs.7
• Ensure Transparency and Explainability in AI Models: The “black box” nature of some AI models poses challenges for trust and accountability.85 Developers should strive for transparency in how AI interprets brain data and makes adaptive decisions, providing users with clear information about data usage and the rationale behind system adaptations.11 Audit trails and mechanisms for human oversight are essential to ensure that AI actions align with organizational goals and ethical standards.85

 

Conclusions

 

Neuroadaptive XR interfaces represent a frontier in human-computer interaction, promising to integrate human cognition directly into digital ecosystems. The convergence of Extended Reality (AR, VR, MR) with Brain-Computer Interface (BCI) technology facilitates a profound shift from traditional, explicit control mechanisms to more intuitive, implicit, and adaptive interactions. This evolution is driven by the ability to interpret real-time neural and physiological signals, allowing XR environments to dynamically respond to a user’s cognitive and emotional states.

The transformative potential of neuroadaptive XR is evident across diverse applications. In gaming and entertainment, it enables unprecedented levels of immersion and personalized experiences by adapting gameplay and narratives to a user’s mental state. In healthcare and rehabilitation, it offers tailored therapeutic interventions, enhancing patient engagement and accelerating recovery for neurological and physical impairments. For military and industrial training, it provides adaptive learning environments that optimize skill acquisition and improve safety by dynamically adjusting to cognitive load. Furthermore, neuroadaptive XR holds the promise of significantly improving accessibility for individuals with disabilities, offering hands-free control and sensory augmentation that can democratize access to digital worlds.

However, the path to widespread adoption is fraught with significant challenges. Technical hurdles, including hardware integration, signal quality, latency, and the need for robust, generalized calibration methods, demand continuous innovation. User experience issues related to headset comfort, potential cognitive overload from overstimulation, and the learning curve associated with novel brain-driven controls must be meticulously addressed through human-centered design. Critically, profound ethical and societal concerns surrounding the privacy and security of intimate neural data, the potential for algorithmic bias, questions of user agency, and the imperative for clear accountability frameworks necessitate proactive and comprehensive governance.

Ultimately, the successful and responsible development of neuroadaptive XR interfaces hinges on a delicate balance between technological advancement and human-centric considerations. Future efforts must prioritize interdisciplinary collaboration among neuroscientists, AI experts, UX designers, ethicists, and policymakers. By focusing on miniaturized, high-fidelity non-invasive BCI, transparent and explainable AI, and robust ethical guidelines, neuroadaptive XR can evolve into a powerful tool that truly augments human capabilities, enhances well-being, and fosters a more inclusive and intuitive digital future. The goal is not merely to control digital environments with our thoughts, but to create a symbiotic relationship where technology seamlessly adapts to and supports the richness of human experience.

Works cited

1. VR, AR, MR, or XR… What’s The Difference? – SkillsVR, accessed on August 3, 2025, https://skillsvr.com/vr-ar-mr-or-xr-whats-the-difference
2. Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR – arXiv, accessed on August 3, 2025, https://arxiv.org/html/2506.05683v3
3. AR, VR and MR | Meta for Work, accessed on August 3, 2025, https://forwork.meta.com/blog/difference-between-vr-ar-and-mr/
4. Computing with AR, MR, and VR: Transforming User Experience – Holoware, accessed on August 3, 2025, https://holoware.co/ar-mr-and-vr-in-computing-revolutionizing-user-experience/
5. VR, AR, MR and What Does Immersion Actually Mean? – Think with Google, accessed on August 3, 2025, https://www.thinkwithgoogle.com/intl/en-emea/future-of-marketing/emerging-technology/vr-ar-mr-and-what-does-immersion-actually-mean/
6. Neuroadaptive Haptics: Comparing Reinforcement Learning from Explicit Ratings and Neural Signals for Adaptive XR Systems – arXiv, accessed on August 3, 2025, https://arxiv.org/html/2504.15984v2
7. www.researchgate.net, accessed on August 3, 2025, https://www.researchgate.net/figure/Schematic-overview-of-neuroadaptive-interface-architecture_fig1_247512094#:~:text=A%20neuroadaptive%20interface%20is%20an,cognitive%20and%2For%20emotional%20states.
8. Neuroadaptive technologies: Applying neuroergonomics to the design of advanced interfaces – ResearchGate, accessed on August 3, 2025, https://www.researchgate.net/publication/247512094_Neuroadaptive_technologies_Applying_neuroergonomics_to_the_design_of_advanced_interfaces
9. Neuroadaptive – Technology, accessed on August 3, 2025, https://neuroadaptive.org/
10. NEUROADAPTIVE AI – Maaind, accessed on August 3, 2025, https://www.maaind.com/neuroadaptiveai
11. Neuroadaptive Interfaces in UX: Adapting Based on Brain Activity – UX Bulletin, accessed on August 3, 2025, https://www.ux-bulletin.com/neuroadaptive-interfaces-ux/
12. Brain–computer interface – Wikipedia, accessed on August 3, 2025, https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface
13. (PDF) Brain-Computer Interface Integration With Extended Reality (XR): Future, Privacy And Security Outlook – ResearchGate, accessed on August 3, 2025, https://www.researchgate.net/publication/381651869_Brain-Computer_Interface_Integration_With_Extended_Reality_XR_Future_Privacy_And_Security_Outlook
14. Editorial: Brain-Computer Interfaces and Augmented/Virtual Reality …, accessed on August 3, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC7235375/
15. RATE: An LLM-Powered Retrieval Augmented Generation Technology-Extraction Pipeline, accessed on August 3, 2025, https://arxiv.org/html/2507.21125v1
16. Extended Reality (XR) & BCI Technology – Zander Labs, accessed on August 3, 2025, https://www.zanderlabs.com/blog/extended-reality-xr-bci-technology
17. Three types of BCI devices/sensors: invasive BCI, semi-invasive BCI, and non-invasive BCI., accessed on August 3, 2025, https://www.researchgate.net/figure/Three-types-of-BCI-devices-sensors-invasive-BCI-semi-invasive-BCI-and-non-invasive-BCI_fig2_373685714
18. What is BCI? An introduction to brain-computer interface using EEG …, accessed on August 3, 2025, https://www.bitbrain.com/blog/brain-computer-interface-using-eeg-signals
19. Ethical and Legal Challenges of Neurotech – DLA Piper, accessed on August 3, 2025, https://www.dlapiper.com/insights/publications/2025/03/ethical-and-legal-challenges-of-neurotech
20. BrainGate – Carney Institute for Brain Science – Brown University, accessed on August 3, 2025, https://carney.brown.edu/research-projects/braingate
21. Mental privacy: navigating risks, rights and regulation: Advances in …, accessed on August 3, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC12287510/
22. The Evolving Landscape of Non-Invasive EEG Brain-Computer Interfaces, accessed on August 3, 2025, https://www.bme.utexas.edu/news/the-evolving-landscape-of-non-invasive-eeg-brain-computer-interfaces
23. Brain computer interface & fNIRS, where we are so far and …, accessed on August 3, 2025, https://brainlatam.com/blog/brain-computer-interface-fnirs-where-we-are-so-far-and-challenges-we-have-ahead-1875
24. Neurogaming: Bridging the Mind and Machine in the Gaming Universe – iMotions, accessed on August 3, 2025, https://imotions.com/blog/insights/trend/neurogaming-bridging-the-mind-and-machine-in-the-gaming-universe/
25. An overview of the pipeline of BCI systems. The figure illustrates the… – ResearchGate, accessed on August 3, 2025, https://www.researchgate.net/figure/An-overview-of-the-pipeline-of-BCI-systems-The-figure-illustrates-the-acquisition-of-a_fig1_379746868
26. Summary of over Fifty Years with Brain-Computer Interfaces—A Review – PMC, accessed on August 3, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC7824107/
27. Survey on the research direction of EEG-based signal … – Frontiers, accessed on August 3, 2025, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1203059/full
28. Brain–computer interface: trend, challenges, and threats – PMC – PubMed Central, accessed on August 3, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC10403483/
29. Brain-Computer Interface: Advancement and Challenges – MDPI, accessed on August 3, 2025, https://www.mdpi.com/1424-8220/21/17/5746
30. Optimizing BCI Performance through Calibration – Number Analytics, accessed on August 3, 2025, https://www.numberanalytics.com/blog/optimizing-bci-performance-through-calibration
31. P300 Brain–Computer Interface-Based Drone Control in Virtual and …, accessed on August 3, 2025, https://www.mdpi.com/1424-8220/21/17/5765
32. Most Popular Signal Processing Methods in Motor-Imagery BCI: A Review and Meta-Analysis – Frontiers, accessed on August 3, 2025, https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2018.00078/full
33. Non-immersive Versus Immersive Extended Reality for Motor Imagery Neurofeedback Within a Brain-Computer Interfaces | Ulster University, accessed on August 3, 2025, https://pure.ulster.ac.uk/files/104860938/Non_immersive_Versus_Immersive_Extended_Reality_for_Motor_Imagery_Neurofeedback_Within_a_Brain_Computer_Interfaces_published_version_DC.pdf
34. Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain-Computer Interfaces – PubMed, accessed on August 3, 2025, https://pubmed.ncbi.nlm.nih.gov/40720262/
35. The P300-Based Brain-Computer Interface (BCI): Effects of Stimulus Rate – PMC, accessed on August 3, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC3050994/
36. Brain–Computer Interface Speller Based on Steady-State Visual …, accessed on August 3, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC8065759/
37. A comparative study of stereo-dependent SSVEP targets and their impact on VR-BCI performance – Frontiers, accessed on August 3, 2025, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1367932/full
38. Defining neuroadaptive technology: the trouble with implicit human-computer interaction | Request PDF – ResearchGate, accessed on August 3, 2025, https://www.researchgate.net/publication/356404992_Defining_neuroadaptive_technology_the_trouble_with_implicit_human-computer_interaction
39. An investigation of a passive BCI’s performance for different body postures and presentation modalities – PubMed, accessed on August 3, 2025, https://pubmed.ncbi.nlm.nih.gov/39946752/
40. Evaluation of an Adaptive Game that Uses EEG Measures Validated during the Design Process as Inputs to a Biocybernetic Loop – Frontiers, accessed on August 3, 2025, https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2016.00223/full
41. [2504.15984] Neuroadaptive Haptics: Comparing Reinforcement Learning from Explicit Ratings and Neural Signals for Adaptive XR Systems – arXiv, accessed on August 3, 2025, https://arxiv.org/abs/2504.15984
42. Enhancing learning experiences: EEG-based passive BCI system adapts learning speed to cognitive load in real-time, with motivation as catalyst – PubMed Central, accessed on August 3, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC11491376/
43. (PDF) Neuroadaptive Training via fNIRS in Flight Simulators – ResearchGate, accessed on August 3, 2025, https://www.researchgate.net/publication/359616374_Neuroadaptive_Training_via_fNIRS_in_Flight_Simulators
44. Enhancing learning experiences: EEG-based passive BCI system adapts learning speed to cognitive load in real-time, with motivation as catalyst – Frontiers, accessed on August 3, 2025, https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2024.1416683/full
45. Neurable: A Game You Can Control With Your Mind [NYT] – UM – Innovation Partnerships, accessed on August 3, 2025, https://innovationpartnerships.umich.edu/stories/neurable-a-game-you-can-control-with-your-mind-nyt/
46. Neuroadaptive Gaming: A Neural Simulation Framework for VR Play Therapy in Children with Autism – ResearchGate, accessed on August 3, 2025, https://www.researchgate.net/publication/393593930_Neuroadaptive_Gaming_A_Neural_Simulation_Framework_for_VR_Play_Therapy_in_Children_with_Autism/download
47. NAT ’19 – Neuroadaptive, accessed on August 3, 2025, https://neuroadaptive.org/wp-content/uploads/2022/07/NAT19_Programme.pdf
48. The Impact of Dynamic Difficulty Adjustment on Player Experience in Video Games, accessed on August 3, 2025, https://digitalcommons.morris.umn.edu/cgi/viewcontent.cgi?article=1105&context=horizons
49. Dynamic Difficulty Adjustment With Brain Waves as a Tool for Optimizing Engagement – arXiv, accessed on August 3, 2025, https://arxiv.org/pdf/2504.13965
50. Games with dynamic difficulty? : r/gamedesign – Reddit, accessed on August 3, 2025, https://www.reddit.com/r/gamedesign/comments/1911i20/games_with_dynamic_difficulty/
51. #Scanners: Exploring the Control of Adaptive Films using Brain-Computer Interaction | Request PDF – ResearchGate, accessed on August 3, 2025, https://www.researchgate.net/publication/291942635_Scanners_Exploring_the_Control_of_Adaptive_Films_using_Brain-Computer_Interaction
52. Virtual Reality Examples: 50+ Real-World VR Applications Across Industries in 2025, accessed on August 3, 2025, https://treeview.studio/blog/virtual-reality-vr-examples
53. eXtended Reality and Artificial Intelligence in Medicine and Rehabilitation – IDEAS/RePEc, accessed on August 3, 2025, https://ideas.repec.org/a/spr/infosf/v27y2025i1d10.1007_s10796-025-10580-8.html
54. XR TECHNOLOGY AND HEALTHCARE, accessed on August 3, 2025, https://xra.org/wp-content/uploads/2021/02/XRA_Slicks_Healthcare_V2.pdf
55. Information About Our XR Therapy System – Neuro Rehab VR, accessed on August 3, 2025, https://neurorehabvr.com/smart-rehab-complete-solution
56. Rewiring Recovery: VR’s Power to Build New Habits – Neuro Rehab VR, accessed on August 3, 2025, https://neurorehabvr.com/blog/vr-builds-habits
57. Combating Stress with Immersive Neurofeedback – SocietyByte, accessed on August 3, 2025, https://www.societybyte.swiss/en/2025/06/03/combating-stress-with-immersive-neurofeedback/
58. A Novel Neurofeedback Attentional Enhancement Approach Based on Virtual Reality, accessed on August 3, 2025, https://pubmed.ncbi.nlm.nih.gov/36085813/
59. XR Tools Reshape Military Training Programs – Halldale Group, accessed on August 3, 2025, https://www.halldale.com/defence/xr-tools-reshape-military-training-programs
60. VR Military Training – Immersive Real-World Scenarios – Luminous XR, accessed on August 3, 2025, https://www.luminousxr.com/vr-military-training/
61. 10 Use Cases in VR For Military Training – Twin Reality, accessed on August 3, 2025, https://twinreality.in/vr-for-military-training/
62. [2507.20943] The Impact of Simple, Brief, and Adaptive Instructions within Virtual Reality Training: Components of Cognitive Load Theory in an Assembly Task – arXiv, accessed on August 3, 2025, https://arxiv.org/abs/2507.20943
63. TNT: Targeted Neuroplasticity Training – DARPA, accessed on August 3, 2025, https://www.darpa.mil/research/programs/targeted-neuroplasticity-training
64. 9 Studying cognitive load in defence – ResearchGate, accessed on August 3, 2025, https://www.researchgate.net/publication/369487935_9_Studying_cognitive_load_in_defence
65. Exploration of an EEG-Based Cognitively Adaptive Training System in Virtual Reality, accessed on August 3, 2025, https://www.researchgate.net/publication/335199515_Exploration_of_an_EEG-Based_Cognitively_Adaptive_Training_System_in_Virtual_Reality
66. CHCI’s Contributions to IEEE VR 2025 | Center for Human-Computer Interaction, accessed on August 3, 2025, https://hci.icat.vt.edu/research/chci-s-contributions-to-ieee-vr-2025.html
67. Minimizing Cognitive Load in Extended Reality Environments – FXMedia, accessed on August 3, 2025, https://www.fxmweb.com/insights/minimizing-cognitive-load-in-extended-reality-environments.html
68. A Comprehensive Review of Multimodal XR Applications, Risks, and Ethical Challenges in the Metaverse – MDPI, accessed on August 3, 2025, https://www.mdpi.com/2414-4088/8/11/98
69. Research | AAC TRANSLATION (AACT) Lab – University of Nebraska–Lincoln, accessed on August 3, 2025, https://aactlab.unl.edu/research/
70. EEG-Based Brain–Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21st Century – PMC – PubMed Central, accessed on August 3, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC5810272/
71. Study Details | Brain-Computer Interface Implant for Severe Communication Disability | ClinicalTrials.gov, accessed on August 3, 2025, https://www.clinicaltrials.gov/study/NCT04576650
72. New Wearable Brain-Computer Interface – Georgia Tech Research, accessed on August 3, 2025, https://research.gatech.edu/new-wearable-brain-computer-interface
73. 5 Ways XR Helps Students with Disabilities – AR Insider, accessed on August 3, 2025, https://arinsider.co/2024/06/19/5-ways-xr-helps-students-with-disabilities/
74. Grand Challenges in Neurotechnology and System Neuroergonomics – Frontiers, accessed on August 3, 2025, https://www.frontiersin.org/journals/neuroergonomics/articles/10.3389/fnrgo.2020.602504/full
75. CONFERENCE PROGRAMME April 7 – 10, 2025, Berlin, Germany – Neuroadaptive, accessed on August 3, 2025, https://neuroadaptive.org/wp-content/uploads/2025/04/NAT25_Conference_Programme.pdf
76. Latency correction of error-related potentials reduces BCI calibration time – Infoscience, accessed on August 3, 2025, https://infoscience.epfl.ch/server/api/core/bitstreams/9da364c2-aae1-4e54-a707-996cc048d653/content
77. Guide :: Latency Calibration is Surprisingly Hard – Steam Community, accessed on August 3, 2025, https://steamcommunity.com/sharedfiles/filedetails/?id=3434111928
78. The problems with XR headsets that even Apple can’t fix | by Amber Case | Jun, 2025, accessed on August 3, 2025, https://caseorganic.medium.com/the-problems-with-xr-headsets-that-even-apple-cant-fix-291502b432c6
79. EPOC X – 14 Channel Wireless EEG Headset – Emotiv, accessed on August 3, 2025, https://www.emotiv.com/products/epoc-x
80. BCI + AR/VR/XR Bundle – OpenBCI Shop, accessed on August 3, 2025, https://shop.openbci.com/products/bcibundle
81. Understanding Cognitive Load in UX and How to Minimize it?, accessed on August 3, 2025, https://www.designstudiouiux.com/blog/what-is-cognitive-load-in-ux/
82. What is Cognitive Load? | IxDF – The Interaction Design Foundation, accessed on August 3, 2025, https://www.interaction-design.org/literature/topics/cognitive-load
83. Ethical Issues Posed by Field Research Using Highly Portable and Cloud-Enabled Neuroimaging, accessed on August 3, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC8803403/
84. Brain Recording, Mind-Reading, and Neurotechnology: Ethical Issues from Consumer Devices to Brain-Based Speech Decoding – PubMed Central, accessed on August 3, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC7417394/
85. Understanding The Limitations Of AI (Artificial Intelligence) | by Mark …, accessed on August 3, 2025, https://medium.com/@marklevisebook/understanding-the-limitations-of-ai-artificial-intelligence-a264c1e0b8ab
86. E3XR: An Analytical Framework for Ethical, Educational and Eudaimonic XR Design, accessed on August 3, 2025, https://www.frontiersin.org/journals/virtual-reality/articles/10.3389/frvir.2021.697667/full
87. Ethical concerns in contemporary virtual reality and frameworks for pursuing responsible use – Frontiers, accessed on August 3, 2025, https://www.frontiersin.org/journals/virtual-reality/articles/10.3389/frvir.2025.1451273/full
88. The emergence of Extended Reality (XR) technologies – WeProtect Global Alliance, accessed on August 3, 2025, https://www.weprotect.org/thematic/extended-reality/
89. The present and future of neural interfaces – Frontiers, accessed on August 3, 2025, https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2022.953968/full
90. The present and future of neural interfaces – PMC – PubMed Central, accessed on August 3, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC9592849/
91. Brain Computer Interface Market Size | Industry Report, 2030 – Grand View Research, accessed on August 3, 2025, https://www.grandviewresearch.com/industry-analysis/brain-computer-interfaces-market
92. Neuroadaptive Haptics: Comparing Reinforcement Learning from Explicit Ratings and Neural Signals for Adaptive XR Systems – arXiv, accessed on August 3, 2025, https://arxiv.org/html/2504.15984v1
93. McKinsey technology trends outlook 2025, accessed on August 3, 2025, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-top-trends-in-tech