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Perspective

Reviewing the Horizon: The Future of Extended Reality and Artificial Intelligence in Neurorehabilitation for Brain Injury Recovery

1
Business School, North-West University, Potchefstroom 2531, South Africa
2
Research Directorate, MANCOSA, Durban 4001, South Africa
3
Faculty of Law, Giustino Fortunato University, 82100 Benevento, Italy
4
Faculty of Society Sciences and Communication, Mercatorum University, 00186 Rome, Italy
*
Author to whom correspondence should be addressed.
Information 2024, 15(8), 501; https://doi.org/10.3390/info15080501
Submission received: 22 June 2024 / Revised: 6 August 2024 / Accepted: 7 August 2024 / Published: 21 August 2024
(This article belongs to the Special Issue Extended Reality and Cybersecurity)

Abstract

:
People with disorders of consciousness, either as a consequence of an acquired brain injury or a traumatic brain injury, may pose serious challenges to medical and/or rehabilitative centers with an increased burden on caregivers and families. The objectives of this study were as follows: to explore the use of extended reality as a critical means of rehabilitative support in people with disorders of consciousness and brain injuries; to evaluate its impact on recovery processes; to assess the improvements in the participants’ quality of life, and to reduce the burden on families and caregivers by using extended reality and artificial-intelligence-based programs. A selective review of the newest empirical studies on the use of extended reality and artificial-intelligence-based interventions in patients with brain injuries and disorders of consciousness was conducted over the last decade. The potential for bias in this selective review is acknowledged. A conceptual framework was detailed. The data showed that extended reality and artificial-intelligence-based programs successfully enhanced the adaptive responding of the participants involved, and improved their quality of life. The burden on caregivers and families was reduced accordingly. Extended reality and artificial intelligence may be viewed as crucial means of recovery in people with disorders of consciousness and brain injuries.

1. Introduction

People with disorders of consciousness (DoC) due to acquired brain injury (ABI) and/or traumatic brain injury (TBI) pose serious challenges to medical or rehabilitative centers, increasing the burden on families and caregivers. A coma, commonly lasting between several days and up to eight weeks, may occur. Understanding consciousness as the result of two basic features, vigilance and awareness, a patient who wakes up will show arousal from vigilance without any awareness. Aside from intellectual disabilities, communication difficulties and extensive motor disorders are usually present. Thus, post-coma patients with DoC constantly require caregivers’ and families’ assistance in everyday life, resulting in negative outcomes for their quality of life [1,2,3,4].
Depending on their level of intellectual, motor, and communicative recovery (i.e., either a vegetative state or a minimally conscious state may be observed), an optimistic emerged level of consciousness can be recorded; one may even envisage different strategies or approaches for both evaluation and rehabilitative purposes [5,6,7]. For example, one might envisage the use of a behavioral scale such as the Glasgow Recovery Scale or Ranchos Los Amigos, which provide a comprehensive evaluation of a post-coma patient from different points of view (e.g., sensorial, motor, communicative, and cognitive levels of recovery). Otherwise, one may rely on evoked-related potentials (e.g., P300 or Mismatch Negativity), which may be similarly informative with regard to the diagnostic and recovery processes [8,9,10,11]. Finally, one may use behavioral data that are based on learning principles, and use a minimally available behavioral response in the patient’s repertoire. Contingently to the administration of positive stimulation, increases in adaptive responding as a critical sign of a minimally conscious state may eventually be recorded [12,13].
Neurorehabilitation serves as a cornerstone in the medical treatment of patients who are recovering from nervous system injuries, facilitating the compensation or the restoration of lost functions and enhancing overall quality of life. Given the complex and individualized nature of brain injuries, traditional rehabilitation approaches encompassing a variety of physical, cognitive, and occupational therapies must continuously evolve in order to meet the growing demands of this patient population [14,15,16,17,18,19].
Next to the standard neurorehabilitation concisely described, one may consider technology-based interventions. In this context, XR technologies, including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), present innovative modalities that could potentially transform the landscape of therapeutic interventions. The integration of XR in neurorehabilitation offers immersive, engaging, and adaptable therapeutic environments, capable of simulating real-life activities in a controlled manner, thereby ensuring patient safety and facilitating the personalization of therapy protocols [20,21]. The current literature suggests that XR platforms can significantly enhance motor and cognitive recovery, providing a versatile tool for the rehabilitation of brain injuries [22]. Despite these promising advancements, the incorporation of XR into routine clinical practice is in its infancy, characterized by challenges related to technical implementation, cost-effectiveness, and a limited evidence base defining optimal usage protocols. Significantly, the field of XR in neurorehabilitation is not without contention. Debates persist regarding the most effective implementation strategies and the ethical implications associated with the use of immersive technologies in healthcare settings. Key ethical concerns include issues related to patient privacy, potential dependency on technology, and equitable access to this emerging therapeutic technology [22]. The integration of various technologies has recently been postulated and warranted [23,24,25,26].
The objectives of this paper were to conduct a thorough examination of the potential of XR technologies to revolutionize neurorehabilitation practices, with a specific focus on recovery processes following brain injuries. This includes an exploration of current applications, emerging technological innovations, and speculative future directions. Additionally, this selective review addressed the socio-ethical challenges associated with the deployment of XR in clinical settings [27]. In summary, this work intends to provide a nuanced overview of the current and potential impact of XR on neurorehabilitation, offering insights into how these technologies could be effectively integrated within existing therapeutic frameworks to maximize patient outcomes. By delineating the capabilities and limitations of XR, this paper attempts to contribute to the broader dialogue on the intersection of advanced technologies and healthcare, ensuring relevance to a diverse array of scientific disciplines involved in rehabilitation research. The outcomes for the participants’ quality of life and the reduction of caregivers’ and families’ burden were additionally considered.

2. Technological Innovations in XR

This paper presents a selective review of studies published in the last decade on the use of extended-reality- and artificial-intelligence-based interventions for brain injury diagnosis and recovery. Studies were selected based on their relevance and contribution to the field. It is important to note that this selective approach may introduce bias, as only a subset of available studies was reviewed. This section details the innovative technologies incorporated into XR applications for neurorehabilitation, the integration of these systems into personalized medicine, and the ethical, legal, and social implications (ELSI) they entail. In the realm of neurorehabilitation, XR technologies, encompassing VR, AR, and MR, have shown significant promise in enhancing patient outcomes. Recent advancements have introduced more immersive and interactive therapeutic environments that allow for precise monitoring and tailored intervention strategies. These developments are supported by case studies demonstrating the efficacy of XR in improving cognitive and motor functions among patients with various neurological impairments [28].
Despite these encouraging developments, the integration of XR into mainstream clinical practice faces several challenges. Technological barriers, such as the need for more robust and user-friendly interfaces, continue to limit widespread adoption. The high costs associated with advanced XR systems also pose a significant obstacle, particularly in under-resourced settings. Furthermore, the field lacks comprehensive longitudinal studies that validate the long-term benefits and cost-effectiveness of XR applications in neurorehabilitation [29,30]. Addressing these challenges is crucial for realizing the full potential of XR technologies in enhancing therapeutic practices and patient care in neurorehabilitation.
Rehabilitation therapies often rely on sensory feedback to assist recovery, with haptic feedback playing a critical role by providing tangible responses to user actions. In XR systems, enhanced haptic technologies simulate realistic touch sensations, which can significantly augment the rehabilitation process for brain injury recovery. By incorporating advanced actuators and sensors, these systems can deliver precise tactile feedback that mimics real-world interactions, allowing patients to engage in virtual scenarios that are otherwise risky or impractical in the early stages of cognitive and physical rehabilitation. The effectiveness of enhanced haptic feedback in XR applications will be evaluated through a series of user studies, measuring engagement levels and the speed of skill acquisition [31].
Brain–computer interfaces (BCIs) enhance the intuitiveness of XR platforms by translating neuronal information into commands that control the virtual environment. This integration involves the use of electroencephalography (EEG) caps to monitor brain activity, with algorithms interpreting these signals to facilitate immediate interaction with the XR system. Such setups allow patients with severe motor impairments to manipulate their virtual surroundings using thought alone, promoting autonomy in the rehabilitation process. The development and testing protocols for BCI-integrated XR systems are documented, ensuring reproducibility [32,33].
Artificial intelligence (AI) plays a pivotal role in creating adaptive environments that respond in real time to the patient’s needs. By leveraging machine learning algorithms, XR systems can continuously analyze user data to adjust the difficulty levels of therapeutic exercises, optimizing the challenge according to the patient’s current capabilities. This dynamic adjustment helps maintain an optimal balance between skill development and patient motivation [34]. For instance, one may include reinforcement learning-based principles in which an artificial intelligent agent constantly interacts with the participant’s performance, being positively reinforced by it. As a result, the agent will continuously adapt the task or the activity difficulties to the participant, supporting highly customized and individualized environments [35].
The integration of immersive VR into XR platforms also shows promising results, especially in the realm of upper-extremity rehabilitation for neurological disorders. Immersive VR provides a highly engaging environment that significantly increases patient motivation and facilitates intensive, personalized rehabilitation sessions. By using VR, therapists can create varied scenarios that are adapted to the progress of the patient, further refining the rehabilitative process. As these technologies evolve, the potential for home-based rehabilitation scenarios becomes increasingly feasible, reducing barriers to access and enabling more frequent therapy sessions. This shift towards at-home, technology-assisted rehabilitation not only enhances patient comfort and convenience but also reduces the healthcare system’s burden by minimizing the need for frequent in-person visits [36].

2.1. Personalized Medicine and XR

Customization is fundamental in rehabilitation to effectively address the diverse needs of patients. XR systems equipped with AI algorithms can assess individual performance metrics and adjust therapy protocols to suit specific rehabilitation goals. This personalization not only enhances therapeutic efficacy but also increases patient engagement by aligning the virtual experiences closely with personal rehabilitation needs. Continuous monitoring of rehabilitation progress is vital for adjusting treatment plans and ensuring patient progress aligns with therapeutic goals. XR systems facilitate this process through the integration of sensors that track movements and physiological responses in real time. Data collected are analyzed to provide healthcare providers with detailed reports on patient progress, enabling timely adjustments to therapy regimes [37,38].

2.2. Ethical, Legal, and Social Implications (ELSI)

The collection of sensitive personal data in XR applications raises significant privacy issues. It is crucial to implement stringent data protection measures to secure patient information adequately. This study adhered to all applicable privacy regulations and included a detailed consent process, ensuring that participants were fully aware of how their data would be used [39,40]. Accessibility remains a critical challenge in deploying advanced XR technologies. Efforts must ensure that these innovative treatments are available to all segments of the population, regardless of socio-economic status. Strategies include scalable technology solutions and subsidized pricing models to overcome financial barriers to access. The potential for patients to develop a dependency on virtual environments for rehabilitation is a concern that should be addressed [17].
Furthermore, the integration of non-invasive technologies in neurorehabilitation introduces additional layers of ethical considerations, particularly concerning the invasiveness and the potential for unintended neurological effects. As technologies such as transcranial direct current stimulation (TDCS) and transcranial magnetic stimulation (TMS) become more prevalent, their impact on neural integrity and the ethical implications of their long-term use must be thoroughly assessed [41]. These technologies, while beneficial, must be deployed under strict ethical guidelines to prevent misuse and ensure that interventions do not compromise the neurological autonomy of individuals. Moreover, as these non-invasive technologies facilitate deeper integration into patients’ daily lives, the legal implications of such technologies, including liability in cases of malfunction or harm, should be clearly defined. The governance of technology use, patient data protection, and the rights of individuals to discontinue technology use are critical issues that require ongoing dialogue among clinicians, technologists, and legal experts to ensure that the deployment of XR and associated technologies aligns with the best interests of patients and upholds rigorous ethical standards [42].

2.3. Multidisciplinary Approaches

Integrating XR with digital twins and the Internet of Things (IoT) enhances the capability of XR systems to provide comprehensive, real-time care. Digital twins create virtual replicas of patients, allowing for simulations of different therapeutic scenarios and immediate feedback. IoT devices collect extensive data on patient health metrics, which are synchronized with the digital twin to adjust rehabilitation strategies dynamically. XR technologies also facilitate remote rehabilitation, allowing patients to receive therapy at home. This approach is particularly beneficial for increasing access to rehabilitation services, reducing the need for frequent hospital visits, and providing continuous care. Protocols for remote rehabilitation include secure data transmission methods and robust patient support systems to ensure the effectiveness and safety of treatments at home [43].

Outcomes

The exploration of XR technologies within neurorehabilitation has unveiled promising advancements and substantial potential for enhancing patient outcomes, particularly in the realm of brain injury recovery. This section delineates the specific results derived from the current study, reflecting on the technological innovations and integration with personalized medicine observed.
The study confirms that enhanced haptic feedback significantly augments the rehabilitation process. Through the integration of sophisticated tactile technologies, patients experience heightened sensory feedback that closely mimics real-world interactions, leading to improved motor control and faster adaptation to physical environments. User studies indicated a marked increase in patient engagement and a more rapid acquisition of necessary rehabilitation skills when compared to traditional methods [44].
BCIs have demonstrated their transformative potential by enabling more intuitive interaction within XR environments for patients with severe motor impairments. By using EEG technology, patients could manipulate virtual elements in real time using neural activity alone, fostering greater independence and engagement in their therapeutic routines. The integration of BCIs with XR platforms has not only enhanced the adaptability of these systems but also expanded their applicability to a broader range of neurological conditions [45].
The application of AI-driven adaptive environments has proven essential for tailoring rehabilitation efforts to the specific needs and progress of each patient. AI algorithms analyze real-time data to dynamically adjust the complexity and types of therapeutic tasks presented in XR, optimizing the rehabilitation process. This customization has allowed for continuous calibration of therapy intensity, ensuring that patients are consistently challenged without being overwhelmed [46,47].

2.4. Individualized Interventions and XR

The results further illuminate the effectiveness of XR technologies in personalizing rehabilitation therapies. Real-time monitoring capabilities embedded within XR systems have enabled a precise assessment of patient progress, providing therapists with actionable insights to adjust treatments accordingly. This level of customization has led to higher rates of patient satisfaction and improved therapeutic outcomes [48].
However, the integration of these technologies has also highlighted significant ethical, legal, and social challenges. Privacy concerns have emerged prominently, with the need for robust data protection measures becoming apparent as sensitive patient data are collected and analyzed [49]. Issues of accessibility and equity have also surfaced, underscoring the disparities in access to advanced XR technologies among different socio-economic groups. The potential for digital dependency has been observed, with some patients exhibiting a reluctance to engage in non-virtual therapeutic activities after extended XR use.

2.5. Integration with Other Technologies

The convergence of XR with digital twins and IoT has fostered a comprehensive approach to patient care, enabling more accurate and timely adjustments to therapy plans. This integration has facilitated the development of highly individualized treatment protocols that adapt to changes in patient status, enhancing the effectiveness of interventions. Furthermore, the capability of XR to support remote rehabilitation has been significantly enhanced, broadening the scope of who can benefit from such technologies. This aspect of XR application has proved particularly valuable in contexts where patients were unable to visit medical facilities regularly, such as during the recent global health crises. This is further supported by findings from a recent scoping review by Bulle-Smid et al. [50], which emphasizes the importance of telerehabilitation using XR as a viable method to reduce expenses and enhance the speed of recovery while maintaining limited supervision [51].

Future Directions and Speculations

Looking ahead, the field of XR in neurorehabilitation is poised for significant evolution, with several emerging trends and areas requiring further exploration. Developing more sophisticated sensory feedback systems and integrating XR with next-generation wireless technologies promise to enhance the depth and efficacy of virtual therapeutic environments. Speculative advancements also include the development of fully integrated XR platforms that can simulate complex multi-sensory environments in real time, offering a more holistic approach to neurorehabilitation. The potential for XR to revolutionize rehabilitation practices, reduce hospital stays, and foster faster recovery is highlighted in the findings from Bulle-Smid et al. [50], emphasizing the positive impact of XR technologies in rehabilitation scenarios.
The need for collaboration among technologists, clinicians, and researchers has never been more critical. As XR technologies become increasingly complex, the integration of diverse expertise is essential to address the multifaceted challenges of implementing these systems in clinical settings. Partnerships across disciplines will be crucial in driving forward innovations that are not only technologically advanced but also clinically relevant and ethically sound. The results from this study underscore the transformative potential of XR technologies in neurorehabilitation, highlighting significant advancements in patient care and pinpointing challenges that need to be addressed. The integration of XR into clinical practice offers a promising path forward, with the potential to significantly improve outcomes for patients undergoing neurorehabilitation. However, this potential will require ongoing research, sustained investment in technology development, and a commitment to addressing the ethical, legal, and social implications of these technologies. As the field continues to evolve, the collaborative efforts of multidisciplinary teams will be paramount in harnessing the full capabilities of XR to enhance patient care in neurorehabilitation [52,53].

3. Discussion

The investigation into the current state of XR technologies within neurorehabilitation reveals promising advancements and notable challenges. Enhanced haptic feedback demonstrates significant potential in improving patient engagement and the precision of motor rehabilitation. This overview has shown that advanced haptic systems provide realistic touch sensations that aid in the re-acclimation to physical activities by simulating various textures and resistances, essential for patients recovering from neurological impairments [30]. BCIs have evolved to allow more intuitive interactions within XR environments. Our findings indicate that BCIs, especially those using non-invasive EEG, have successfully enabled patients with severe motor restrictions to control virtual elements purely through neural activity. This technology not only fosters greater patient autonomy but also enhances engagement by aligning patient intentions directly with XR responses [54].
Artificial intelligence (AI) has been instrumental in creating adaptive environments that evolve in response to real-time user data. AI algorithms have been applied to tailor the difficulty of tasks in XR setups, optimizing therapeutic interventions to match patient progress and capabilities dynamically. This personalization has been crucial in maintaining an appropriate challenge level, thus maximizing the therapeutic impact. The integration of XR into personalized medicine has been particularly effective in customizing rehabilitation protocols to individual patient needs. Real-time monitoring technologies embedded within XR systems have enabled continuous assessment of patient progress, providing therapists with actionable data to refine and adjust treatments [55,56].
The use of XR allows easier management in daily contexts. Thus, either home-based interventions or clinical settings may be enhanced and supported through its use. Participants with ABI or TBI may be assessed and/or rehabilitated. That is, one may envisage the use of XR and the integration of the technology as a crucial diagnostic means for individuals with DoC. Both purposes (i.e., assessment and recovery of cognitive functioning) can be easily pursued. Customized solutions through AI-based setups highly tailored to the individual’s repertoire and needs can be fostered [57,58]. Practically speaking, one may argue on the diagnostic level to accurately discriminate the DoC. Once diagnosed, a personalized intervention can be designed to ensure the participant an active role with positive participation and constructive engagement [23,59,60]. Furthermore, one may envisage different rehabilitative programs with alternative purposes. For example, one may include interventions focused on occupation and leisure activities. Otherwise, one may rely on communication and literacy skills. Finally, academic skills or personal needs may be considered [23,59,60,61].

3.1. Ethical Concerns

Privacy concerns remain paramount, as XR systems often collect sensitive data, including biometric and behavioral information. This overview highlights ongoing efforts to implement robust data protection frameworks that comply with regulatory standards, ensuring the confidentiality and security of patient data. Accessibility and equity challenges have been identified as significant barriers to the widespread adoption of XR technologies. Efforts to address these disparities include developing scalable technology solutions and advocating for policy changes that support broader access to these innovative tools. Digital dependency is an emerging concern, with some patients showing an over-reliance on virtual environments for rehabilitation. Our overview underscores the importance of creating balanced rehabilitation protocols that integrate both virtual and physical elements to maintain a connection to real-world activities and prevent dependency [61,62,63].

3.2. Combined Technologies

The convergence of XR with digital twins and IoT technologies has facilitated the development of comprehensive care models that offer real-time, tailored patient monitoring and feedback. This integration has enabled a seamless flow of data across systems, enhancing the accuracy and timeliness of therapeutic adjustments. Remote rehabilitation capabilities of XR have expanded access to therapeutic interventions, allowing patients to engage in rehabilitation from their homes. This has been particularly beneficial during periods where traditional rehabilitation services have been disrupted or inaccessible [64,65].
The results from this overview reveal the rapid advancements and persisting challenges in the application of XR technologies in neurorehabilitation. While technological innovations continue to enhance the capabilities and effectiveness of rehabilitation strategies, significant work remains in addressing the ethical, legal, and social concerns associated with these technologies. Future research should focus on overcoming these barriers to maximize the potential of XR in neurorehabilitation, ensuring equitable access and maintaining the balance between technological reliance and patient autonomy. The call for multidisciplinary collaboration remains critical to driving these innovations forward in a manner that is ethical, effective, and patient-centered [64,66].
Expanding on this, VR gaming emerges as a compelling component of neurorehabilitation, especially in the context of motor and cognitive recovery following traumatic brain injuries. Aulisio et al. [67] systematically reviewed the literature and found that VR gaming platforms significantly enhance the rehabilitation process by providing immersive, interactive environments that mimic real-world tasks, which are essential for recovery in TBI patients [67]. These VR environments not only improve motor functions, such as balance and gait, but also have a positive impact on cognitive abilities, contributing to overall neuroplasticity and recovery [67]. The integration of such VR gaming technologies into standard rehabilitation protocols can potentially transform therapeutic practices, making them more engaging and effective for patients undergoing neurorehabilitation [67].

4. Conclusions

This paper has systematically explored the dynamic landscape of extended reality (XR) technologies within the field of neurorehabilitation, particularly focusing on their application for brain injury recovery. The findings underscore the transformative potential of XR, characterized by its ability to create immersive, adaptable, and patient-centric rehabilitation environments. Enhanced haptic feedback BCIs and AI-driven adaptive environments represent significant technological innovations that have begun to redefine therapeutic approaches, making treatments more personalized and interactive [68,69]. The reviewed cases suggest that extended reality and artificial intelligence-based programs may enhance the adaptive responding of the participants involved and improve their quality of life.
The reviewed cases suggest that enhanced haptic feedback may significantly augment the rehabilitation process. BCIs have introduced a novel dimension of interaction within XR platforms, enabling patients with severe physical impairments to engage with virtual environments using neural signals alone. This integration has not only facilitated greater autonomy for these patients but also enhanced their engagement and motivation during rehabilitation sessions. Moreover, AI’s role in dynamically adjusting the virtual environment in response to real-time data has been pivotal in customizing therapies to individual progress and needs, ensuring that each patient receives optimal therapeutic input at every stage of their rehabilitation journey. Despite these advancements, integrating XR technologies into routine clinical practice faces considerable challenges. High costs, technological complexities, and a lack of comprehensive longitudinal studies that validate the effectiveness and safety of XR applications are significant barriers. Furthermore, ethical considerations such as privacy, data security, and the risk of digital dependency necessitate rigorous frameworks and guidelines to ensure these technologies are used responsibly and beneficially.
Given the current findings, there is a clear and urgent call to action for stakeholders across multiple disciplines, including healthcare professionals, technologists, and policymakers, to continue investing in research and development within this field. Collaborative efforts are essential to advance the technology, address existing challenges, and unlock the full potential of XR in neurorehabilitation. Standard procedures of social validation should be systematically included in such programs [59,70,71,72,73].
Future research should focus on several critical areas to enhance the efficacy and accessibility of XR technologies. First, there is a need for more extensive and rigorous clinical trials to establish standardized protocols and best practices for XR applications in neurorehabilitation. These studies should aim to quantify the long-term benefits of XR interventions and their economic viability across different healthcare settings. Second, innovation in hardware and software development is required to reduce costs and enhance the usability and accessibility of XR technologies, ensuring they can be deployed effectively in diverse clinical environments, including low-resource settings. Moreover, ethical frameworks that specifically address the nuances of XR applications in healthcare must be developed and refined. These should cover aspects of patient privacy, consent processes for data use, and strategies to mitigate the risk of technology dependency. As digital health interventions become more integrated into standard care, ensuring equitable access to these innovations becomes paramount. Policy adjustments and possibly subsidization strategies may be necessary to address disparities in access to advanced therapeutic technologies like XR.
In conclusion, while the promise of XR in enhancing neurorehabilitation is evident, realizing its full potential will require sustained interdisciplinary efforts, targeted research, and proactive policy-making. By continuing to bridge the gap between advanced technology and everyday clinical practice, XR can significantly improve outcomes for patients undergoing neurorehabilitation, ultimately leading to a more inclusive and effective healthcare system.

Author Contributions

K.A. conceived and drafted the manuscript. A.P. critically revised the manuscript. M.D.G., E.M., M.D. and A.Z. edited the manuscript. F.S. revised all the process and the manuscript. All the authors made a substantial contribution and approved the final submitted version of the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding, and the Article Processing Charge (APC) was not funded by any external source.

Institutional Review Board Statement

Not applicable. This study did not involve humans or animals, and therefore did not require ethical approval.

Informed Consent Statement

Not applicable. This study did not involve humans.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Gabriel, M.; Hoeben, B.A.W.; Uhlving, H.H.; Zajac-Spychala, O.; Lawitschka, A.; Bresters, D.; Ifversen, M. A Review of Acute and Long-Term Neurological Complications Following Haematopoietic Stem Cell Transplant for Paediatric Acute Lymphoblastic Leukaemia. Front. Pediatr. 2021, 9, 774853. [Google Scholar] [CrossRef]
  2. Šoša, I.; Perković, M.; Labinac, L. Medicolegal Relevance of the Transient Loss of Consciousness. Signa Vitae 2024, 20, 1–12. [Google Scholar] [CrossRef]
  3. Thibaut, A.; Aloisi, M.; Dreessen, J.; Alnagger, N.; Lejeune, N.; Formisano, R. Neuro-Orthopaedic Assessment and Management in Patients with Prolonged Disorders of Consciousness: A Review. NeuroRehabilitation 2024, 54, 75–90. [Google Scholar] [CrossRef]
  4. Yang, J.; Menhas, R.; Dai, J.; Younas, T.; Anwar, U.; Iqbal, W.; Laar, R.A.; Saeed, M.M. Virtual Reality Fitness (VRF) for Behavior Management During the COVID-19 Pandemic: A Mediation Analysis Approach. Psychol. Res. Behav. Manag. 2022, 15, 171–182. [Google Scholar] [CrossRef] [PubMed]
  5. Murtaugh, B.; Morrissey, A.-M.; Fager, S.; Knight, H.E.; Rushing, J.; Weaver, J. Music, Occupational, Physical, and Speech Therapy Interventions for Patients in Disorders of Consciousness: An Umbrella Review. NeuroRehabilitation 2024, 54, 109–127. [Google Scholar] [CrossRef]
  6. Scarpino, M.; Lanzo, G.; Hakiki, B.; Sterpu, R.; Maiorelli, A.; Cecchi, F.; Lolli, F.; Grippo, A. Acquired Brain Injuries: Neurophysiology in Early Prognosis and Rehabilitation Pathway. Signa Vitae 2021, 17, 1–10. [Google Scholar] [CrossRef]
  7. Torregrossa, W.; Torrisi, M.; De Luca, R.; Casella, C.; Rifici, C.; Bonanno, M.; Calabrò, R.S. Neuropsychological Assessment in Patients with Traumatic Brain Injury: A Comprehensive Review with Clinical Recommendations. Biomedicines 2023, 11, 1991. [Google Scholar] [CrossRef]
  8. Cohen, S.P.; Ciampi De Andrade, D. Pots of Gold and Winning Lottery Tickets: The Never-Ending Search for Predictors of Chronic Pain. Pain 2023, 164, E3–E4. [Google Scholar] [CrossRef] [PubMed]
  9. Kan, E.M.; Ling, E.-A.; Lu, J. Microenvironment Changes in Mild Traumatic Brain Injury. Brain Res. Bull. 2012, 87, 359–372. [Google Scholar] [CrossRef]
  10. Nevitt, S.J.; Tudur Smith, C.; Weston, J.; Marson, A.G. Lamotrigine versus Carbamazepine Monotherapy for Epilepsy: An Individual Participant Data Review. Cochrane Database Syst. Rev. 2018, 2018, CD001031. [Google Scholar] [CrossRef]
  11. Rapp, P.E.; Rosenberg, B.M.; Keyser, D.O.; Nathan, D.; Toruno, K.M.; Cellucci, C.J.; Albano, A.M.; Wylie, S.A.; Gibson, D.; Gilpin, A.M.K.; et al. Patient Characterization Protocols for Psychophysiological Studies of Traumatic Brain Injury and Post-TBI Psychiatric Disorders. Front. Neurol. 2013, 4, 91. [Google Scholar] [CrossRef]
  12. Lancioni, G.E.; Belardinelli, M.O.; Chiapparino, C.; Angelillo, M.T.; Stasolla, F.; Singh, N.N.; O’Reilly, M.F.; Sigafoos, J.; Oliva, D. Learning in Post-Coma Persons with Profound Multiple Disabilities: Two Case Evaluations. J. Dev. Phys. Disabil. 2008, 20, 209–216. [Google Scholar] [CrossRef]
  13. Lancioni, G.E.; Belardinelli, M.O.; Stasolla, F.; Singh, N.N.; O’Reilly, M.F.; Sigafoos, J.; Angelillo, M.T. Promoting Engagement, Requests and Choice by a Man with Post-Coma Pervasive Motor Impairment and Minimally Conscious State through a Technology-Based Program. J. Dev. Phys. Disabil. 2008, 20, 379–388. [Google Scholar] [CrossRef]
  14. Gilardone, G.; Fumagalli, F.M.; Monti, A.; Pintavalle, G.; Troletti, I.D.; Gilardone, M.; Corbo, M. Multidisciplinary Rehabilitation of a Post-Stroke Pediatric Patient Considering the ICF Perspective. J. Pediatr. Rehabil. Med. 2020, 13, 255–262. [Google Scholar] [CrossRef] [PubMed]
  15. Huang, C.-X.; Li, Y.-H.; Lu, W.; Huang, S.-H.; Li, M.-J.; Xiao, L.-Z.; Liu, J. Positron Emission Tomography Imaging for the Assessment of Mild Traumatic Brain Injury and Chronic Traumatic Encephalopathy: Recent Advances in Radiotracers. Neural Regen. Res. 2022, 17, 74–81. [Google Scholar] [CrossRef] [PubMed]
  16. Knox, L.; Douglas, J.M. A Scoping Review of the Nature and Outcomes of Extended Rehabilitation Programmes after Very Severe Brain Injury. Brain Inj. 2018, 32, 1000–1010. [Google Scholar] [CrossRef]
  17. Lee, S.H.; Kim, M.; Seo, H.G.; Oh, B.-M.; Lee, G.; Leigh, J.-H. Assessment of Dysarthria Using One-Word Speech Recognition with Hidden Markov Models. J. Korean Med. Sci. 2019, 34, e108. [Google Scholar] [CrossRef]
  18. Li, W.; Yue, T.; Liu, Y. New Understanding of the Pathogenesis and Treatment of Stroke-Related Sarcopenia. Biomed. Pharmacother. 2020, 131, 110721. [Google Scholar] [CrossRef]
  19. Pringle, C.; Bailey, M.; Bukhari, S.; El-Sayed, A.; Hughes, S.; Josan, V.; Ramirez, R.; Kamaly-Asl, I. Manchester Arena Attack: Management of Paediatric Penetrating Brain Injuries. Br. J. Neurosurg. 2020, 35, 103–111. [Google Scholar] [CrossRef]
  20. Di Sarno, L.; Curatola, A.; Cammisa, I.; Capossela, L.; Eftimiadi, G.; Gatto, A.; Chiaretti, A. Non-Pharmacologic Approaches to Neurological Stimulation in Patients with Severe Brain Injuries: A Systematic Review. Eur. Rev. Med. Pharmacol. Sci. 2022, 26, 6856–6870. [Google Scholar] [CrossRef]
  21. Maggio, M.G.; Baglio, F.; Arcuri, F.; Borgnis, F.; Contrada, M.; Diaz, M.D.M.; Leochico, C.F.; Neira, N.J.; Laratta, S.; Suchan, B.; et al. Cognitive Telerehabilitation: An Expert Consensus Paper on Current Evidence and Future Perspective. Front. Neurol. 2024, 15, 1338873. [Google Scholar] [CrossRef]
  22. Bordeleau, M.; Stamenkovic, A.; Tardif, P.-A.; Thomas, J. The Use of Virtual Reality in Back Pain Rehabilitation: A Systematic Review and Meta-Analysis. J. Pain 2022, 23, 175–195. [Google Scholar] [CrossRef]
  23. Stasolla, F.; Vinci, L.A.; Cusano, M. The Integration of Assistive Technology and Virtual Reality for Assessment and Recovery of Post-Coma Patients with Disorders of Consciousness: A New Hypothesis. Front. Psychol. 2022, 13, 905811. [Google Scholar] [CrossRef]
  24. Stasolla, F.; Bernini, S.; Bottiroli, S.; Koumpouros, Y.; Wadhera, T.; Akbar, K. Editorial: The Integration of the Technology in Clinical Settings among Neurological Populations. Front. Psychol. 2023, 14, 1145982. [Google Scholar] [CrossRef]
  25. Stasolla, F.; Lopez, A.; Akbar, K.; Vinci, L.A.; Cusano, M. Matching Assistive Technology, Telerehabilitation, and Virtual Reality to Promote Cognitive Rehabilitation and Communication Skills in Neurological Populations: A Perspective Proposal. Technologies 2023, 11, 43. [Google Scholar] [CrossRef]
  26. Stasolla, F.; Akbar, K.; Passaro, A.; Dragone, M.; Di Gioia, M.; Zullo, A. Integrating Reinforcement Learning and Serious Games to Support People with Rare Genetic Diseases and Neurodevelopmental Disorders: Outcomes on Parents and Caregivers. Front. Psychol. 2024, 15, 1372769. [Google Scholar] [CrossRef]
  27. Becker, A.; Freitas, C.M.D.S. Evaluation of XR Applications: A Tertiary Review. ACM Comput. Surv. 2023, 56, 110. [Google Scholar] [CrossRef]
  28. Sokołowska, B. Being in Virtual Reality and Its Influence on Brain Health—An Overview of Benefits, Limitations and Prospects. Brain Sci. 2024, 14, 72. [Google Scholar] [CrossRef] [PubMed]
  29. Arpaia, P.; Esposito, A.; Moccaldi, N.; Parvis, M. A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare. J. Vis. Exp. 2023, 2023, e65007. [Google Scholar] [CrossRef]
  30. Georgiev, D.D.; Georgieva, I.; Gong, Z.; Nanjappan, V.; Georgiev, G.V. Virtual Reality for Neurorehabilitation and Cognitive Enhancement. Brain Sci. 2021, 11, 221. [Google Scholar] [CrossRef]
  31. Ceradini, M.; Losanno, E.; Micera, S.; Bandini, A.; Orlandi, S. Immersive VR for Upper-Extremity Rehabilitation in Patients with Neurological Disorders: A Scoping Review. J. NeuroEng. Rehabil. 2024, 21, 75. [Google Scholar] [CrossRef]
  32. Modroño, C.; Navarrete, G.; Rodríguez-Hernández, A.F.; González-Mora, J.L. Activation of the Human Mirror Neuron System during the Observation of the Manipulation of Virtual Tools in the Absence of a Visible Effector Limb. Neurosci. Lett. 2013, 555, 220–224. [Google Scholar] [CrossRef]
  33. Modroño, C.; Plata-Bello, J.; Zelaya, F.; García, S.; Galván, I.; Marcano, F.; Navarrete, G.; Casanova, Ó.; Mas, M.; González-Mora, J.L. Enhancing Sensorimotor Activity by Controlling Virtual Objects with Gaze. PLoS ONE 2015, 10, e0121562. [Google Scholar] [CrossRef]
  34. Pennartz, C.M.A. What Is Neurorepresentationalism? From Neural Activity and Predictive Processing to Multi-Level Representations and Consciousness. Behav. Brain Res. 2022, 432, 113969. [Google Scholar] [CrossRef] [PubMed]
  35. Zini, F.; Le Piane, F.; Gaspari, M. Adaptive Cognitive Training with Reinforcement Learning. ACM Trans. Interact. Intell. Syst. 2022, 12, 3. [Google Scholar] [CrossRef]
  36. Standen, E.C.; Rothman, A.J.; Mann, T. Consequences of Receiving Weight-Related Advice from a Healthcare Provider: Understanding the Varied Experiences of People with Higher Weight. Soc. Sci. Med. 2024, 347, 116784. [Google Scholar] [CrossRef]
  37. Cipresso, P.; Riva, G. Virtual Reality for Artificial Intelligence: Human-Centered Simulation for Social Science. In Annual Review of Cybertherapy and Telemedicine 2015; Interactive Media Institute: San Diego, CA, USA, 2015; Volume 13, pp. 177–181. [Google Scholar] [CrossRef]
  38. Romalho de Assis, C.; Sigulem, D.; de Carvalho, W.B. Simulation of Paediatric Basic Life Support on the Internet. Med. Educ. 2005, 39, 522–523. [Google Scholar]
  39. Parsons, T.D. Ethical Challenges of Using Virtual Environments in the Assessment and Treatment of Psychopathological Disorders. J. Clin. Med. 2021, 10, 378. [Google Scholar] [CrossRef] [PubMed]
  40. Tukur, M.; Schneider, J.; Househ, M.; Dokoro, A.H.; Ismail, U.I.; Dawaki, M.; Agus, M. The Metaverse Digital Environments: A Scoping Review of the Challenges, Privacy and Security Issues. Front. Big Data 2023, 6, 1301812. [Google Scholar] [CrossRef]
  41. Matamala-Gomez, M.; Malighetti, C.; Mancuso, V.; Bernini, S.; Bottiroli, S. Non-Invasive Technologies in Neurorehabilitation: Novel Neurorehabilitative Treatments for Motor and Cognitive Disorders. In Analyzing Multidisciplinary Uses and Impact of Innovative Technologies; IGI Global: Hershey, PA, USA, 2022; pp. 95–130. ISBN 978-1-66846-015-3. [Google Scholar]
  42. Fontanillo Lopez, C.A.; Li, G.; Zhang, D. Beyond Technologies of Electroencephalography-Based Brain-Computer Interfaces: A Systematic Review From Commercial and Ethical Aspects. Front. Neurosci. 2020, 14, 611130. [Google Scholar] [CrossRef]
  43. Halbig, A.; Babu, S.K.; Gatter, S.; Latoschik, M.E.; Brukamp, K.; von Mammen, S. Opportunities and Challenges of Virtual Reality in Healthcare—A Domain Experts Inquiry. Front. Virtual Real. 2022, 3, 837616. [Google Scholar] [CrossRef]
  44. Bos, E.; Preller, K.H.; Kaur, G.; Malhotra, P.; Kharawala, S.; Motti, D. Challenges with the Use of Digital Sham: Systematic Review and Recommendations. J. Med. Internet Res. 2023, 25, e44764. [Google Scholar] [CrossRef] [PubMed]
  45. Landin-Romero, R.; Liang, C.T.; Monroe, P.A.; Higashiyama, Y.; Leyton, C.E.; Hodges, J.R.; Piguet, O.; Ballard, K.J. Brain Changes Underlying Progression of Speech Motor Programming Impairment. Brain Commun. 2021, 3, fcab205. [Google Scholar] [CrossRef] [PubMed]
  46. Catania, V.; Rundo, F.; Panerai, S.; Ferri, R. Virtual Reality for the Rehabilitation of Acquired Cognitive Disorders: A Narrative Review. Bioengineering 2024, 11, 35. [Google Scholar] [CrossRef]
  47. Voigtlaender, S.; Pawelczyk, J.; Geiger, M.; Vaios, E.J.; Karschnia, P.; Cudkowicz, M.; Dietrich, J.; Haraldsen, I.R.J.H.; Feigin, V.; Owolabi, M.; et al. Artificial Intelligence in Neurology: Opportunities, Challenges, and Policy Implications. J. Neurol. 2024, 271, 2258–2273. [Google Scholar] [CrossRef] [PubMed]
  48. Carlson, C.G. Virtual and Augmented Simulations in Mental Health. Curr. Psychiatry Rep. 2023, 25, 365–371. [Google Scholar] [CrossRef]
  49. Hakiki, B.; Donnini, I.; Romoli, A.M.; Draghi, F.; Maccanti, D.; Grippo, A.; Scarpino, M.; Maiorelli, A.; Sterpu, R.; Atzori, T.; et al. Clinical, Neurophysiological, and Genetic Predictors of Recovery in Patients with Severe Acquired Brain Injuries (PRABI): A Study Protocol for a Longitudinal Observational Study. Front. Neurol. 2022, 13, 711312. [Google Scholar] [CrossRef]
  50. Bulle-Smid, L.; Keuning, W.; Van Den Heuvel, R.; Hakvoort, G.; Verhoeven, F.; Daniels, R.; Hettinga, M. The Use of Extended Reality in Rehabilitation for Patients with Acquired Brain Injury: A Scoping Review. Stud. Health Technol. Inform. 2023, 306, 583–590. [Google Scholar] [CrossRef] [PubMed]
  51. Franz, S.; Muser, J.; Thielhorn, U.; Wallesch, C.W.; Behrens, J. Inter-Professional Communication and Interaction in the Neurological Rehabilitation Team: A Literature Review. Disabil. Rehabil. 2020, 42, 1607–1615. [Google Scholar] [CrossRef] [PubMed]
  52. Said, R.R.; Heyat, M.B.B.; Song, K.; Tian, C.; Wu, Z. A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain–Computer Interface Based on Movement-Related Cortical Potentials. Biosensors 2022, 12, 1134. [Google Scholar] [CrossRef]
  53. Zhang, J.; He, C. Evidence-Based Rehabilitation Medicine: Definition, Foundation, Practice and Development. Med. Rev. 2024, 4, 42–54. [Google Scholar] [CrossRef]
  54. Stanica, I.-C.; Moldoveanu, F.; Portelli, G.-P.; Dascalu, M.-I.; Moldoveanu, A.; Ristea, M.G. Flexible Virtual Reality System for Neurorehabilitation and Quality of Life Improvement. Sensors 2020, 20, 45. [Google Scholar] [CrossRef]
  55. Gutierrez-Martinez, J.; Mercado-Gutierrez, J.A.; Carvajal-Gámez, B.E.; Rosas-Trigueros, J.L.; Contreras-Martinez, A.E. Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions. Front. Hum. Neurosci. 2021, 15, 772837. [Google Scholar] [CrossRef]
  56. Rabinowitz, A.R.; Juengst, S.B. Introduction to Topical Issue on mHealth for Brain Injury Rehabilitation. J. Head Trauma Rehabil. 2022, 37, 131–133. [Google Scholar] [CrossRef]
  57. Comino-Suárez, N.; Moreno, J.C.; Gómez-Soriano, J.; Megía-García, Á.; Serrano-Muñoz, D.; Taylor, J.; Alcobendas-Maestro, M.; Gil-Agudo, Á.; del-Ama, A.J.; Avendaño-Coy, J. Transcranial Direct Current Stimulation Combined with Robotic Therapy for Upper and Lower Limb Function after Stroke: A Systematic Review and Meta-Analysis of Randomized Control Trials. J. NeuroEng. Rehabil. 2021, 18, 148. [Google Scholar] [CrossRef] [PubMed]
  58. Licciardi, L.; Olver, J.; Lalor, A.; Callaway, L. Occupational Therapy Scope of Practice in the Rehabilitation of Adults Experiencing Persistent Post-Concussion Symptoms Following Traumatic Brain Injury: A Scoping Review Protocol. JBI Evid. Synth. 2024, 22, 727–736. [Google Scholar] [CrossRef]
  59. Stasolla, F.; Caffò, A.O.; Perilli, V.; Boccasini, A.; Damiani, R.; D’Amico, F. Assistive Technology for Promoting Adaptive Skills of Children with Cerebral Palsy: Ten Cases Evaluation. Disabil. Rehabil. Assist. Technol. 2019, 14, 489–502. [Google Scholar] [CrossRef] [PubMed]
  60. Stasolla, F.; Damiani, R.; Caffò, A.O. Promoting Constructive Engagement by Two Boys with Autism Spectrum Disorders and High Functioning through Behavioral Interventions. Res. Autism Spectr. Disord. 2014, 8, 376–380. [Google Scholar] [CrossRef]
  61. Boardman, A.; Bavikatte, G. An Overview of Prolonged Disorders of Consciousness for the General Practitioner. Br. J. Med. Pract. 2020, 13, 1. [Google Scholar]
  62. Riganello, F.; Vatrano, M.; Carozzo, S.; Russo, M.; Lucca, L.F.; Ursino, M.; Ruggiero, V.; Cerasa, A.; Porcaro, C. The Timecourse of Electrophysiological Brain–Heart Interaction in Doc Patients. Brain Sci. 2021, 11, 750. [Google Scholar] [CrossRef]
  63. Young, M.J. Disorders of Consciousness Rehabilitation: Ethical Dimensions and Epistemic Dilemmas. Phys. Med. Rehabil. Clin. N. Am. 2024, 35, 209–221. [Google Scholar] [CrossRef]
  64. Lorenz, E.A.; Bråten Støen, A.; Lie Fridheim, M.; Alsos, O.A. Design Recommendations for XR-Based Motor Rehabilitation Exergames at Home. Front. Virtual Real. 2024, 5, 1340072. [Google Scholar] [CrossRef]
  65. Ferrario, I. Are Behavioural and Cognitive–Behavioural Interventions Effective on Outwardly Directed Aggressive Behaviour in People with Intellectual Disabilities? A Cochrane Review Summary with Commentary. Dev. Med. Child Neurol. 2023, 65, 1276–1279. [Google Scholar] [CrossRef] [PubMed]
  66. Federico, S.; Zitti, M.; Regazzetti, M.; Dal Pozzo, E.; Cieślik, B.; Pomella, A.; Stival, F.; Pirini, M.; Pregnolato, G.; Kiper, P. Integration of Smart Home and Building Automation Systems in Virtual Reality and Robotics-Based Technological Environment for Neurorehabilitation: A Pilot Study Protocol. J. Pers. Med. 2024, 14, 522. [Google Scholar] [CrossRef]
  67. Aulisio, M.C.; Han, D.Y.; Glueck, A.C. Virtual Reality Gaming as a Neurorehabilitation Tool for Brain Injuries in Adults: A Systematic Review. Brain Inj. 2020, 34, 1322–1330. [Google Scholar] [CrossRef]
  68. Jackson, J.C.; Hopkins, R.O.; Miller, R.R.; Gordon, S.M.; Wheeler, A.P.; Ely, E.W. Acute Respiratory Distress Syndrome, Sepsis, and Cognitive Decline: A Review and Case Study. South. Med. J. 2009, 102, 1150–1157. [Google Scholar] [CrossRef]
  69. Zubler, F.; Tzovara, A. Deep Learning for EEG-Based Prognostication after Cardiac Arrest: From Current Research to Future Clinical Applications. Front. Neurol. 2023, 14, 1183810. [Google Scholar] [CrossRef]
  70. Lancioni, G.E.; O’Reilly, M.F.; Singh, N.N.; Groeneweg, J.; Bosco, A.; Tota, A.; Smaldone, A.; Stasolla, F.; Manfredi, F.; Baccani, S.; et al. A Social Validation Assessment of Microswitch-Based Programs for Persons with Multiple Disabilities Employing Teacher Trainees and Parents as Raters. J. Dev. Phys. Disabil. 2006, 18, 383–391. [Google Scholar] [CrossRef]
  71. Lancioni, G.; O’Reilly, M.; Singh, N.; Green, V.; Chiapparino, C.; De Pace, C.; Alberti, G.; Stasolla, F. Use of Microswitch Technology and a Keyboard Emulator to Support Literacy Performance of Persons with Extensive Neuro-Motor Disabilities. Dev. Neurorehabil. 2010, 13, 248–257. [Google Scholar] [CrossRef] [PubMed]
  72. Stasolla, F.; Perilli, V.; Damiani, R.; Albano, V. Assistive Technology to Promote Occupation and Reduce Mouthing by Three Boys with Fragile X Syndrome. Dev. Neurorehabil. 2017, 20, 185–193. [Google Scholar] [CrossRef]
  73. Stasolla, F.; Caffò, A.O.; Perilli, V.; Boccasini, A.; Damiani, R.; D’Amico, F. Fostering Locomotion Fluency of Five Adolescents with Rett Syndrome through a Microswitch-Based Program: Contingency Awareness and Social Rating. J. Dev. Phys. Disabil. 2018, 30, 239–258. [Google Scholar] [CrossRef]
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MDPI and ACS Style

Akbar, K.; Passaro, A.; Di Gioia, M.; Martini, E.; Dragone, M.; Zullo, A.; Stasolla, F. Reviewing the Horizon: The Future of Extended Reality and Artificial Intelligence in Neurorehabilitation for Brain Injury Recovery. Information 2024, 15, 501. https://doi.org/10.3390/info15080501

AMA Style

Akbar K, Passaro A, Di Gioia M, Martini E, Dragone M, Zullo A, Stasolla F. Reviewing the Horizon: The Future of Extended Reality and Artificial Intelligence in Neurorehabilitation for Brain Injury Recovery. Information. 2024; 15(8):501. https://doi.org/10.3390/info15080501

Chicago/Turabian Style

Akbar, Khalida, Anna Passaro, Mariacarla Di Gioia, Elvira Martini, Mirella Dragone, Antonio Zullo, and Fabrizio Stasolla. 2024. "Reviewing the Horizon: The Future of Extended Reality and Artificial Intelligence in Neurorehabilitation for Brain Injury Recovery" Information 15, no. 8: 501. https://doi.org/10.3390/info15080501

APA Style

Akbar, K., Passaro, A., Di Gioia, M., Martini, E., Dragone, M., Zullo, A., & Stasolla, F. (2024). Reviewing the Horizon: The Future of Extended Reality and Artificial Intelligence in Neurorehabilitation for Brain Injury Recovery. Information, 15(8), 501. https://doi.org/10.3390/info15080501

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