1. Introduction
Spasticity, a common consequence of stroke, is a significant health issue and a primary cause of long-term disability. It frequently leads to substantial motor impairments that inhibit mobility and independence [1]. To address these impairments, effective rehabilitation strategies are crucial for improving the quality of life for stroke survivors. While traditional rehabilitation methods are beneficial, they may not fully meet the diverse needs of all patients. This has sparked increased interest in advanced technologies that can enhance rehabilitation outcomes.
The Brain-Computer Interface (BCI) is a promising technology that facilitates direct communication between the brain and external devices, bypassing damaged neural pathways. The potential for real-time feedback and personalized control is what makes Electroencephalogram (EEG)-based BCIs particularly appealing. These devices utilize non-invasive electroencephalography to capture brain activity.
Recent advancements have focused on integrating EEG-based BCIs with various rehabilitation technologies to further enhance their effectiveness. Key areas of integration include functional electrical stimulation (FES) to stimulate muscles, sensory stimulation technologies like augmented reality (AR) and virtual reality (VR) to create immersive training environments, and robotic systems to support and guide movements [2].
This work aims to explore how EEG-based BCIs are integrated with these rehabilitation technologies to improve motor functions in stroke survivors. By examining the effectiveness, challenges, and clinical outcomes of each approach, this review provides a comprehensive understanding of BCI-integrated neurorehabilitation and suggests future directions for research and practice."
2. Integration of EEG and FES for Stroke Rehabilitation
The application of electrical currents to stimulate muscles and facilitate movement is known as FES. It is frequently employed in neurorehabilitation to facilitate motor recovery by stimulating contractions in paralyzed or compromised muscles. In stroke survivors, FES has been demonstrated to enhance coordination, muscle strength, and motor function [3]. The primary benefit of FES is its capacity to deliver targeted muscle stimulation, which can aid in the restoration of functional movements and facilitate motor relearning.
The integration of EEG-based BCIs with FES represents a significant advancement in neurorehabilitation, combining real-time brain activity monitoring with muscle stimulation. EEG-based BCIs can interpret brain signals associated with motor intention and provide feedback to control FES systems. This approach allows for more personalized and adaptive rehabilitation interventions [4].
There are several advantages of the integration. First, enhanced control and adaptation. EEG-based BCIs can capture the user’s motor intentions and adjust the FES parameters accordingly. This real-time interaction enables a more precise and responsive rehabilitation process, tailoring the stimulation to the patient’s current state and goals. Studies have demonstrated that this integration can lead to improved motor function outcomes compared to traditional FES alone [5]. Second, neuroplasticity promotion. By delivering both cortical and peripheral stimulation, the integration of EEG-based BCIs and FES can improve neural plasticity. By more effectively engaging the brain's plasticity mechanisms, this synergistic effect can facilitate motor learning and recovery [6]. Research suggests that synchronized stimulation can result in more substantial changes in brain activity patterns and improved motor function.
However, there are also several clinical outcomes and challenges. Clinical trials have shown promising results in integrating EEG-based BCIs with FES. For instance, patients have reported improvements in muscle strength, motor control, and functional independence [5]. Nevertheless, there are obstacles, such as the need for more robust, accurate, and fast online algorithms, the lengthy training time, user fatigue, and the complexity of calibrating and setting up the systems [7]. It is imperative to confront these obstacles in order to optimize the practical implementation and efficacy of these integrated systems in clinical environments.
3. Integration of EEG and AR/VR for Stroke Rehabilitation
AR and VR are advanced technologies that stimulate the senses and create settings that fully engage the user. Neurorehabilitation has seen a growing utilization of these technologies to offer patients captivating and inspiring experiences. AR and VR have the ability to replicate real-world activities and surroundings, allowing individuals to engage in motor skill exercises in a supervised, secure, and replicable manner [8,9]. The sensory input offered by these systems can improve the process of acquiring motor skills and cognitive involvement, which are crucial for successful rehabilitation [8,9].
The integration of AR and VR technologies with EEG-based BCIs is a substantial advancement in the field of neurorehabilitation. These systems can offer feedback and adaptive training that is customized by integrating immersive sensory environments with real-time brain activity monitoring.
The integration provides real-time feedback and adaptation. EEG-based BCIs can monitor the patient’s brain activity and provide immediate feedback, which is crucial for enhancing learning and engagement. For instance, in a VR environment, an EEG-based BCI can detect a patient’s level of attention or cognitive load and adjust the difficulty of tasks accordingly [10]. This adaptive feedback mechanism ensures that the rehabilitation exercises remain challenging yet achievable, which is essential for sustained motivation and progress [11].
The integration of BCIs with AR and VR not only offers a more immersive sensory experience but also improves the brain's ability to reorganize and adapt, known as neuroplasticity. Research has demonstrated that involving both the motor and sensory systems through these technologies can result in more substantial enhancements in motor functions [12]. The instantaneous feedback obtained from EEG signals aids in strengthening desired movements and deterring wrong ones, hence facilitating enhanced motor learning.
However, there are still several challenges. The use of EEG-based BCIs with AR/VR in clinical settings has shown promising results. Patients often report increased motivation, enjoyment, and adherence to rehabilitation protocols when these technologies are employed [13,14]. Improvements have been observed in motor function, cognitive abilities, and overall rehabilitation outcomes [14]. However, challenges such as the need for high-quality EEG signal acquisition, the design of user-friendly interfaces, hardware compatibility and the cost of VR/AR setups can limit widespread adoption [13]. Addressing these challenges will be critical for integrating these technologies into standard clinical practice.
4. Integration of EEG and Robotic Systems for Stroke Rehabilitation
Robotic systems have become essential tools in modern stroke rehabilitation, providing consistent, precise, and intensive motor training. Unlike traditional rehabilitation methods, robotic systems facilitate highly controlled and repetitive task-specific movements that are critical for retraining motor pathways and promoting neuroplasticity [15]. These systems can be used for both upper and lower limb rehabilitation, providing the necessary physical assistance to enhance the effectiveness of rehabilitation, which is vital for improving functional independence [15,16]. Additionally, robotic systems help reduce the physical burden on therapists, ensuring that patients receive a high volume of consistent and targeted practice [17].
Integrating EEG-based BCIs with robotic systems represents a unique and advanced approach in neurorehabilitation. These systems create a closed-loop feedback mechanism that directly translates brain signals into robotic movements, allowing for real-time, adaptive interactions. This approach not only aids in motor assistance for individuals with severe impairments but also promotes functional recovery by enhancing neuroplasticity and facilitating targeted rehabilitation [18].
Unlike FES, which primarily stimulates muscle contractions, and AR/VR, which focuses on sensory feedback and virtual environments, BCI-robotic systems engage patients in active, motor-driven rehabilitation. By interpreting brain signals and converting them into precise robotic movements, these systems directly involve the patient in the motor task. This direct engagement helps to stimulate neuroplastic changes, which are essential for recovery from motor impairments [19]. The robotic device provides assistance or resistance based on real-time feedback, actively involving the patient’s brain in the rehabilitation process. This approach facilitates the reorganization of motor pathways, essential for restoring motor function. Research has shown that BCI-robotic systems can drive significant improvements in motor outcomes by continuously adapting to the patient’s progress and maintaining the optimal level of challenge.
While FES concentrates on muscle activation and AR/VR offers immersive experiences, BCI-robotic systems provide a distinctive blend of mental engagement and physical interaction. These systems not only assist in the execution of movements but also in the maintenance of patient motivation and participation over extended periods. A dynamic and responsive rehabilitation environment is established by the capacity to provide immediate, brain-based feedback. The robotic system can dynamically modify the complexity of tasks to ensure that the patient remains challenged but not overwhelmed, as it responds to the patient's cognitive and motor states [20]. This ongoing adaptation is essential for maintaining patients' engagement and motivation, which is essential for the successful attainment of long-term rehabilitation objectives.
Research on the integration of EEG-based BCIs with autonomous systems has demonstrated significant enhancements in motor function, muscle strength, and coordination, as quantified by the Fugl-Meyer Assessment (FMA) and the Action Research Arm Test (ARAT) [20]. These outcomes are promising, but several challenges remain, particularly in the areas of:
Unlike the comparatively simple feedback mechanisms in FES and AR/VR, BCI-robotic systems necessitate intricate signal processing in order to appropriately interpret EEG signals. Robust algorithms are required to accurately interpret motor intentions in EEG signals, which are affected by individual variances and environmental interference [20].
Two primary problems commonly restrict the utility and accessibility of BCI-robotic systems. Many robotic devices are designed based on anthropometric measures that may not be compatible with the different body types and sizes of the patients they are meant to assist [21]. This discrepancy can diminish the efficacy of the devices and restrict their suitability for certain patient demographics. Furthermore, the exorbitant expenses associated with procuring and upkeeping these advanced technologies render them financially unattainable for numerous institutions [21]. The expenses associated with BCI-robotic technologies, as well as the requirement for specific training for patients and healthcare professionals, provide substantial obstacles to the general implementation of these technologies in clinical settings.
5. Discussion
The integration of EEG-based BCIs with various rehabilitation technologies shows significant potential for improving motor recovery in stroke survivors. Each method—FES, Sensory Stimulation Technologies (AR/VR), and External Machine-Assisted Methods (Robotic Systems)—offers distinct advantages and presents unique challenges. In this discussion, the author examines the strengths, limitations, and potential of each approach, comparing their contributions to neurorehabilitation.
5.1. Effectiveness of motor function recovery
Among the three technologies, EEG-based BCI combined with FES stands out for its ability to directly stimulate muscles, promoting muscle contractions and aiding in motor recovery. This method is particularly effective in delivering targeted muscle stimulation, which helps restore functional movements in individuals with severe motor impairments. The integration of EEG with FES provides more precise and personalized rehabilitation by tailoring stimulation based on real-time brain signals. However, the complexity of signal processing and the extended training times required can lead to user fatigue, limiting its overall impact.
In contrast, EEG-based BCIs integrated with Sensory Stimulation Technologies (AR/VR) emphasize motor learning by engaging both sensory and motor systems. These immersive environments provide real-time feedback, which enhances motivation and engagement. This method is especially beneficial for improving cognitive and motor skills through simulated real-world tasks. However, AR/VR is less effective in directly stimulating muscles compared to FES and presents challenges in acquiring high-quality EEG signals and ensuring hardware compatibility. While it offers an adaptive and personalized rehabilitation experience, AR/VR may not be as effective for patients with severe physical impairments who require more direct muscle activation.
EEG-based BCIs integrated with robotic systems offer a unique combination of physical support and mental engagement. Unlike FES, which focuses on muscle contractions, or AR/VR, which emphasizes sensory feedback, robotic systems provide controlled, repetitive movements that are essential for retraining motor pathways. These systems excel in offering continuous, consistent assistance that promotes neuroplasticity through interactive motor learning. The ability of robotic systems to dynamically adjust assistance based on the patient’s progress ensures that the patient remains appropriately challenged. However, the high cost of robotic systems and design limitations, such as anthropometric mismatches, restrict their widespread use.
5.2. Challenges and Accessibility
A key challenge across all three methods is the complexity of EEG signal processing. Accurately interpreting brain signals to control external devices remains difficult due to variability in signals and external interference. This highlights the need for more robust algorithms that can improve the accuracy and speed of signal decoding. When it comes to usability and accessibility, AR/VR systems are generally more affordable and flexible compared to FES and robotic systems. However, the lack of physical interaction limits AR/VR’s effectiveness for patients with severe motor impairments.
Robotic systems, while offering superior physical support and interactive rehabilitation, face significant barriers related to cost and accessibility. The design of these devices often does not account for the diverse body types of the patient population, reducing their effectiveness. Additionally, the high cost of acquiring and maintaining these advanced technologies makes them inaccessible to many institutions. This financial burden, coupled with the need for specialized training for patients and healthcare providers, limits the broader implementation of robotic systems in clinical practice.
5.3. Neuroplasticity and long-term rehabilitation
All three methods contribute to neuroplasticity, but in different ways. FES achieves this by delivering both cortical and peripheral stimulation, which reinforces neural connections in the brain. AR/VR promotes cognitive engagement and motor learning, helping to reorganize the brain’s neural networks through immersive environments and task simulations. Robotic systems, however, offer the most direct approach to facilitating neuroplastic changes by engaging patients in active, motor-driven rehabilitation. The real-time feedback and interaction provided by robotic systems play a crucial role in long-term motor recovery by continuously adapting to the patient’s progress.
6. Conclusion
In summary, the three methods—FES, AR/VR, and robotic systems—each offer unique benefits to stroke rehabilitation. However, the integration of EEG-based BCIs with robotic systems stands out as the most comprehensive solution, particularly for patients with severe motor impairments. Robotic systems excel by combining physical assistance with mental engagement, providing a highly interactive and adaptive rehabilitation process. Nevertheless, the high cost and limited accessibility of these systems need to be addressed to increase their availability. FES is effective for stimulating muscle contractions but faces challenges such as user fatigue and complex calibration. On the other hand, AR/VR offers an engaging and accessible rehabilitation environment, yet it lacks the physical interaction necessary for patients with significant impairments. Future research should focus on reducing the cost and complexity of robotic systems, enhancing signal processing algorithms, and exploring hybrid approaches that leverage the strengths of FES, AR/VR, and robotics. By combining these technologies, people can develop more effective and adaptable rehabilitation platforms. Addressing these challenges is crucial for unlocking the full potential of EEG-based BCI-integrated rehabilitation technologies and improving motor recovery outcomes for stroke survivors.
References
[1]. Patel, M. D., Tilling, K., Lawrence, E., Rudd, A. G., Wolfe, C. D. A., & McKevitt, C. (2006). Relationships between long-term stroke disability, handicap and health-related quality of life. Age and Ageing, 35(3), 273-279.
[2]. Biasiucci, A., Leeb, R., Iturrate, I., Perdikis, S., Al-Khodairy, A., Corbet, T., ... & Millán, J. D. R. (2018). Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nature communications, 9(1), 2421.
[3]. Rushton, D. N. (2003). Functional electrical stimulation and rehabilitation—an hypothesis. Medical engineering & physics, 25(1), 75-78.
[4]. McCrimmon, C. M., King, C. E., Wang, P. T., Cramer, S. C., Nenadic, Z., & Do, A. H. (2014). Brain-controlled functional electrical stimulation for lower-limb motor recovery in stroke survivors. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1247-1250.
[5]. Jiang, S., Chen, L., Wang, Z., Xu, J., Qi, C., Qi, H., ... & Ming, D. (2015). Application of BCI-FES system on stroke rehabilitation. In 2015 7th international ieee/embs conference on neural engineering, 1112-1115.
[6]. Khan, M. A., Das, R., Iversen, H. K., & Puthusserypady, S. (2020). Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application. Computers in biology and medicine, 123, 103843.
[7]. Abiri, R., Borhani, S., Sellers, E. W., Jiang, Y., & Zhao, X. (2019). A comprehensive review of EEG-based brain–computer interface paradigms. Journal of neural engineering, 16(1), 011001.
[8]. Levac, D. E., Huber, M. E., & Sternad, D. (2019). Learning and transfer of complex motor skills in virtual reality: a perspective review. Journal of neuroengineering and rehabilitation, 16, 1-15.
[9]. Desai, K., Bahirat, K., Ramalingam, S., Prabhakaran, B., Annaswamy, T., & Makris, U. E. (2016). Augmented reality-based exergames for rehabilitation. In Proceedings of the 7th International Conference on Multimedia Systems, 1-10.
[10]. Douibi, K., Le Bars, S., Lemontey, A., Nag, L., Balp, R., & Breda, G. (2021). Toward EEG-based BCI applications for industry 4.0: Challenges and possible applications. Frontiers in Human Neuroscience, 15, 705064.
[11]. Pinto, J. F., Carvalho, H. R., Chambel, G. R., Ramiro, J., & Gonçalves, A. (2018). Adaptive gameplay and difficulty adjustment in a gamified upper-limb rehabilitation. In 2018 IEEE 6th international conference on serious games and applications for health, 1-8.
[12]. Zhuang, M., Wu, Q., Wan, F., & Hu, Y. (2020). State-of-the-art non-invasive brain–computer interface for neural rehabilitation: A review. Journal of Neurorestoratology, 8(1), 12-25.
[13]. Si-Mohammed, H., Petit, J., Jeunet, C., Argelaguet, F., Spindler, F., Evain, A., ... & Lécuyer, A. (2018). Towards BCI-based interfaces for augmented reality: feasibility, design and evaluation. IEEE transactions on visualization and computer graphics, 26(3), 1608-1621.
[14]. Vourvopoulos, A., Jorge, C., Abreu, R., Figueiredo, P., Fernandes, J. C., & Bermudez i Badia, S. (2019). Efficacy and brain imaging correlates of an immersive motor imagery BCI-driven VR system for upper limb motor rehabilitation: A clinical case report. Frontiers in human neuroscience, 13, 244, 1-10.
[15]. Xu, B., Peng, S., Song, A., Yang, R., & Pan, L. (2011). Robot-aided upper-limb rehabilitation based on motor imagery EEG. International Journal of Advanced Robotic Systems, 8(4), 40, 1-10.
[16]. Tariq, M., Trivailo, P. M., & Simic, M. (2018). EEG-based BCI control schemes for lower-limb assistive-robots. Frontiers in human neuroscience, 12, 312, 1-13.
[17]. Clark, W. E., Sivan, M., & O’Connor, R. J. (2019). Evaluating the use of robotic and virtual reality rehabilitation technologies to improve function in stroke survivors: A narrative review. Journal of rehabilitation and assistive technologies engineering, 6, 1-7.
[18]. Liu, L., Jin, M., Zhang, L., Zhang, Q., Hu, D., Jin, L., & Nie, Z. (2022). Brain–computer interface-robot training enhances upper extremity performance and changes the cortical activation in stroke patients: a functional near-infrared spectroscopy study. Frontiers in neuroscience, 16, 809657, 1-13.
[19]. Ang, K. K., Guan, C., Chua, K. S. G., Ang, B. T., Kuah, C., Wang, C., ... & Zhang, H. (2010). Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 5549-5552.
[20]. Robinson, N., Mane, R., Chouhan, T., & Guan, C. (2021). Emerging trends in BCI-robotics for motor control and rehabilitation. Current Opinion in Biomedical Engineering, 20, 100354, 1-18.
[21]. Quiles, E., Suay, F., Candela, G., Chio, N., Jiménez, M., & Álvarez-Kurogi, L. (2020). Low-cost robotic guide based on a motor imagery brain–computer interface for arm assisted rehabilitation. International journal of environmental research and public health, 17(3), 699, 1-16.
Cite this article
Zhuang,Z. (2024). Advancements in EEG-Based Brain-Computer Interface Integrations for Stroke Rehabilitation. Applied and Computational Engineering,106,167-173.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Patel, M. D., Tilling, K., Lawrence, E., Rudd, A. G., Wolfe, C. D. A., & McKevitt, C. (2006). Relationships between long-term stroke disability, handicap and health-related quality of life. Age and Ageing, 35(3), 273-279.
[2]. Biasiucci, A., Leeb, R., Iturrate, I., Perdikis, S., Al-Khodairy, A., Corbet, T., ... & Millán, J. D. R. (2018). Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nature communications, 9(1), 2421.
[3]. Rushton, D. N. (2003). Functional electrical stimulation and rehabilitation—an hypothesis. Medical engineering & physics, 25(1), 75-78.
[4]. McCrimmon, C. M., King, C. E., Wang, P. T., Cramer, S. C., Nenadic, Z., & Do, A. H. (2014). Brain-controlled functional electrical stimulation for lower-limb motor recovery in stroke survivors. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1247-1250.
[5]. Jiang, S., Chen, L., Wang, Z., Xu, J., Qi, C., Qi, H., ... & Ming, D. (2015). Application of BCI-FES system on stroke rehabilitation. In 2015 7th international ieee/embs conference on neural engineering, 1112-1115.
[6]. Khan, M. A., Das, R., Iversen, H. K., & Puthusserypady, S. (2020). Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application. Computers in biology and medicine, 123, 103843.
[7]. Abiri, R., Borhani, S., Sellers, E. W., Jiang, Y., & Zhao, X. (2019). A comprehensive review of EEG-based brain–computer interface paradigms. Journal of neural engineering, 16(1), 011001.
[8]. Levac, D. E., Huber, M. E., & Sternad, D. (2019). Learning and transfer of complex motor skills in virtual reality: a perspective review. Journal of neuroengineering and rehabilitation, 16, 1-15.
[9]. Desai, K., Bahirat, K., Ramalingam, S., Prabhakaran, B., Annaswamy, T., & Makris, U. E. (2016). Augmented reality-based exergames for rehabilitation. In Proceedings of the 7th International Conference on Multimedia Systems, 1-10.
[10]. Douibi, K., Le Bars, S., Lemontey, A., Nag, L., Balp, R., & Breda, G. (2021). Toward EEG-based BCI applications for industry 4.0: Challenges and possible applications. Frontiers in Human Neuroscience, 15, 705064.
[11]. Pinto, J. F., Carvalho, H. R., Chambel, G. R., Ramiro, J., & Gonçalves, A. (2018). Adaptive gameplay and difficulty adjustment in a gamified upper-limb rehabilitation. In 2018 IEEE 6th international conference on serious games and applications for health, 1-8.
[12]. Zhuang, M., Wu, Q., Wan, F., & Hu, Y. (2020). State-of-the-art non-invasive brain–computer interface for neural rehabilitation: A review. Journal of Neurorestoratology, 8(1), 12-25.
[13]. Si-Mohammed, H., Petit, J., Jeunet, C., Argelaguet, F., Spindler, F., Evain, A., ... & Lécuyer, A. (2018). Towards BCI-based interfaces for augmented reality: feasibility, design and evaluation. IEEE transactions on visualization and computer graphics, 26(3), 1608-1621.
[14]. Vourvopoulos, A., Jorge, C., Abreu, R., Figueiredo, P., Fernandes, J. C., & Bermudez i Badia, S. (2019). Efficacy and brain imaging correlates of an immersive motor imagery BCI-driven VR system for upper limb motor rehabilitation: A clinical case report. Frontiers in human neuroscience, 13, 244, 1-10.
[15]. Xu, B., Peng, S., Song, A., Yang, R., & Pan, L. (2011). Robot-aided upper-limb rehabilitation based on motor imagery EEG. International Journal of Advanced Robotic Systems, 8(4), 40, 1-10.
[16]. Tariq, M., Trivailo, P. M., & Simic, M. (2018). EEG-based BCI control schemes for lower-limb assistive-robots. Frontiers in human neuroscience, 12, 312, 1-13.
[17]. Clark, W. E., Sivan, M., & O’Connor, R. J. (2019). Evaluating the use of robotic and virtual reality rehabilitation technologies to improve function in stroke survivors: A narrative review. Journal of rehabilitation and assistive technologies engineering, 6, 1-7.
[18]. Liu, L., Jin, M., Zhang, L., Zhang, Q., Hu, D., Jin, L., & Nie, Z. (2022). Brain–computer interface-robot training enhances upper extremity performance and changes the cortical activation in stroke patients: a functional near-infrared spectroscopy study. Frontiers in neuroscience, 16, 809657, 1-13.
[19]. Ang, K. K., Guan, C., Chua, K. S. G., Ang, B. T., Kuah, C., Wang, C., ... & Zhang, H. (2010). Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 5549-5552.
[20]. Robinson, N., Mane, R., Chouhan, T., & Guan, C. (2021). Emerging trends in BCI-robotics for motor control and rehabilitation. Current Opinion in Biomedical Engineering, 20, 100354, 1-18.
[21]. Quiles, E., Suay, F., Candela, G., Chio, N., Jiménez, M., & Álvarez-Kurogi, L. (2020). Low-cost robotic guide based on a motor imagery brain–computer interface for arm assisted rehabilitation. International journal of environmental research and public health, 17(3), 699, 1-16.