Introduction
Parkinson’s Disease (PD) is a progressive neurodegenerative disorder characterized by motor deficiency symptoms such as tremors, rigidity, and bradykinesia (slowness of movement). PD affects about 11 million people worldwide (WHO, 2021). Beyond motor dysfunctions, individuals with PD often experience cognitive impairments, including brain dysfunction, memory impairment, and difficulties with attention and problem-solving. Studies suggest that between 40% and 60% of PD patients will develop significant cognitive impairment as the disease progresses (Fang et al., 2020).
Brain-computer interface (BCI) technology has emerged as a revolutionary approach to managing both motor and cognitive impairments associated with PD. By translating neural activity into algorithmic commands, BCI enables direct communication between the brain and external devices (Lebedev and Nicolelis, 2006). This remarkable ability improves motor rehabilitation through direct intervention at the sites on damaged pathways, allowing neural signals to be relayed and eventually performed by devices such as robotic limbs.
Understanding the underlying cognitive mechanisms that govern memory, attention, and problem-solving is crucial for optimal BCI interventions. Cognitive psychology allows us to examine these processes, providing insights into how to enhance the effectiveness of BCI technology. For instance, rehabilitation techniques targeted at enhancing cognitive function in PD patients heavily rely on neuroplasticity—the brain’s ability to restructure itself by creating new neural connections (Kleim & Jones, 2008). This paper examines the role of BCI technology in enhancing motor and cognitive functions for individuals with Parkinson’s disease and the integration of cognitive psychology principles into BCI design.
Parkinson’s Disease: Motor and Cognitive Deficits
Impact on Motor Function
PD substantially impairs motor functions, with common symptoms including bradykinesia and stiffness. Such impairments make simple daily tasks such as walking or dressing up in the morning become a greater challenge for PD patients (Rajput et al., 1993). Such motor symptoms not only hinder physical capabilities but also deter self-independence and increase the necessity of medical aid, often increasing frustration and emotional distress among patients.
Impact on Cognitive Function
Patients with PD often have cognitive deficiencies, including difficulties with memory, attention, and executive function (Jellinger, 2024). Studies have shown that these cognitive impairments are due to the neural destruction that occurs in key brain regions, such as the basal ganglia and prefrontal cortex, which are critical for both motor control and cognitive processing (Ghaffari et al., 2019). Specifically, damage to the basal ganglia has been linked to effects in decision- making and task execution, while prefrontal cortex impairment has been shown to decrease functions in areas such as concentration and organization (Cosgrove et al., 2018). As a result, these cognitive deficits make PD patients less independent and lower their quality of life.
Relation to Cognitive Psychology
Understanding the symptoms of PD through the lens of cognitive psychology offers great insight into the frameworks of these impairments, such as dual-task interference, which is the difficulty in performing various tasks simultaneously. This challenge hints at a problem far greater than cognitive impairment; it highlights greater deficits in attentional allocation and executive functioning. Cognitive theories, specifically those dealing with limited capacity processing and neuroplasticity, give us a deeper explanation as to why PD patients are vulnerable to such interference. For example, a study by O’Shea et al. (2002) found that when PD patients were asked to walk while performing a mental arithmetic task, their gait speed decreased by nearly 19%, and stride length shortened, indicating that cognitive load significantly impairs motor control.
Cognitive therapies designed to stimulate neuroplasticity, where the brain adapts by forming new neural connections, can enhance recovery in PD. For example, a structured training protocol by Tzeng, Chiu, and Wu (2018) improved learning accuracy (p < 0.05) and memory retention in participants after sustained cognitive stimulation, underscoring the role of targeted exercises in strengthening neural pathways.
How does BCI technology operate?
BCI technology presents a groundbreaking approach to addressing motor deficits in PD. These systems create a direct communication pathway between the brain and external devices, enabling neural signals to bypass or compensate for damaged pathways. Modern BCIs, which have shown promising results in enhancing precision, usability, and rehabilitation outcomes, are increasingly exploring adaptive closed-loop designs, (Jin et al., 2024) . In research settings, real-time calibration to an individual’s neural activity patterns has improved performance and user experience. Understanding the operational mechanisms of BCI technology along with its applications and the distinctions between invasive and non-invasive methods offers a comprehensive perspective on this emerging path to rehabilitation and cognitive enhancement
Signal Acquisition, Processing, and Output Generation
BCIs work by taking in brain impulses, interpreting the signal, and translating them into commands that control computers or other devices. The process usually consists of three stages:
Signal Acquisition: This initial stage begins by recording the brain’s electrical activity. Typically, this is accomplished by measuring brain waves using electrodes placed on the scalp, i.e., electroencephalography (EEG). EEG is widely favored for its cost-effectiveness and non-invasive protocol, though—as discussed in Technical Challenges—it can be prone to movement artifacts, noise interference, and electrode displacement (Lui et al., 2025). In the case of invasive approaches, specialists will commonly implant electrodes like ECoG or intracortical arrays; these ultimately offer enhanced spatial resolution as well as higher-bandwidth signal, both of which are critical for precise control of neuroprosthetics. (Waldert, 2016)
Signal Processing: After the signal has been acquired, the raw electrical signals are filtered to remove noise and detect meaningful patterns that correspond to specific thoughts or desired outcomes. To enhance the accuracy and reliability of these readings, a multi-step signal processing pipeline incorporates real-time frequency analysis and signal filtration using machine learning algorithms. Techniques such as common spatial patterns, wavelet transforms, and support vector machines have been shown to significantly improve EEG classification accuracy for Parkinson’s patients, enabling more precise translation of neural activity into device commands (Khezri et al., 2022).
Output Generation: Finally, the processed signals are translated into commands that can be executed with the help of a computer or external device, permitting the user to perform tasks such as moving a computer cursor, controlling a prosthetic limb, or simply communicating with computer interfaces (Vidaurre & Blankertz, 2010).
Review of Clinical Trials and Case Studies
BCI technology has proven to be useful for helping people recover from stroke, traumatic brain injuries, or degenerative illnesses like Parkinson’s, supporting both motor and cognitive rehabilitation (López-Larraz et al. 2018). Designed to address each patient’s specific neural patterns and motor objectives. This level of adaptability from BCI fuels greater effective motor rehabilitation. In a study that featured a non-invasive motor imagery-based BCI protocol, seven PD patients took part in 14 training sessions, with each session lasting from 1.5 to 2 hours and ranging from 2 to 3 times per week. The results showed promising data, with classification accuracy rates surpassing 70% for lower-limb motor imagery task (Miladinovic et al., 2020)
Beyond motor implications, it possesses several cognitive functions as well. A pilot study case done by Turconi et al. (2014) saw three PD patients undergoing 15 sessions of imagery-based EEG-BCI training. Despite this, the results showed a reduction in freezing of gait and heightened alpha and beta EEG, which are critical biomarkers for neural activity. On top of that, patients witnessed greater performance in attention and executive tasks after engaging in this intervention.
Expanding on these findings, a study in non-invasive EEG-based neurofeedback by Lavermiocca et al. (2018) brought ten PD patients with mild cognitive impairment. Each was ascribed to complete 24 training sessions over the course of three months. Harnessing the technology of BCI, the participants received real-time feedback on their brain/neural activity. After 3 months a significant improvement was seen in multiple cognitive domains such as attention, executive function, visuospatial reasoning, and immediate and delayed memory recall.
These findings highlight how both invasive and noninvasive BCI modalities are being explored in clinical contexts, making it important to compare their distinct advantages, limitations, and suitability for various patient populations.
Comparison Between Invasive and Non-Invasive BCIs
BCIs are classified into two types: invasive and non-invasive systems. Invasive BCIs, as the name suggests, necessitate surgical implantation of electrodes into the brain tissue. This key feature allows them to boast exceptional signaling and long-term stability, two key aspects for refining motor control (Waldert 2016). In fact, patients using intracortical electrodes have maintained successful control of robotic limbs for over 1,000 days post-implant, demonstrating long-term viability of invasive BCI systems in real-world use (Simeral et al., 2011).
On the other hand, non-invasive BCI establishes a neural interface connection that links brain activity to assistive systems. However, this does not require surgical implantation; instead, it utilizes external sensors such as EEG. Because these sensors are put on the scalp rather than implanted in the body, noninvasive BCIs are often seen as safer and more economical as a result. (Caiado & Ukolov, 2025). Recent studies have shown significant performance improvements: for instance, Tibrewal et al. (2022) reported that deep-learning classifiers improved motor-imagery EEG classification accuracy by 2.4% to 28.3% across a group of 54 participants, highlighting the real-world benefits of modern BCI algorithms.
Mechanisms of Motor Control Restoration
BCIs promote cortical reorganization by directly engaging motor networks in task-specific activities. Continuous neural monitoring enables precise, real-time adjustments during rehabilitation, ensuring optimal conditions for relearning movement control (Kruse et al., 2020).
In a case involving a patient with advanced Parkinson’s disease (PD), a BCI system allowed direct brain control of cursor movements on a computer screen. The findings from this case indicated not only an improvement in fine motor control but also enhanced patient motivation as they began to see progression in their recovery, underscoring the psychosocial benefits alongside physical improvements (Lebedev & Nicolelis, 2006).
Furthermore, BCIs have demonstrated the ability to activate key motor- related brain regions such as the primary motor cortex, premotor cortex, and supplementary motor area, effectively rerouting resources from damaged pathways to healthier ones to support motor tasks. Moreover, it also engages subcortical areas, including the basal ganglia-thalamocortical circuit, that play a crucial role in movement. The capability of BCI to uniquely target activation can enhance the effectiveness of rehabilitation exercises, allowing patients to practice movements in a controlled environment where they can receive immediate feedback and reinforcement (Lebedev & Nicolelis, 2006).
Comparison with Existing Treatments: Deep Brain Stimulation (DBS)
While BCIs show revolutionary potential, current treatments such as deep brain stimulation (DBS) also provide a considerable amount of benefits for PD-related motor control. DBS involves the implantation of electrodes into certain brain regions, delivering electrical impulses that can help regulate abnormal brain activity associated with PD symptoms (Fiorillo et al., 2019). Studies have shown that DBS can lead to significant improvements in motor function, particularly by reducing symptoms such as tremors and bradykinesia. These improvements often contribute to a better overall quality of life for patients with movement disorders, including PD. (Kringelbach et al., 2020).
When analyzing the differences between BCIs and DBS, there are several factors worth considering. Invasiveness is a crucial one; while DBS demands surgical intervention, BCIs have the option of avoiding surgery, allowing for greater accessibility and reduced risk (Fiorillo et al., 2019) as well as the possibility of being more affordable. Adaptability is also a crucial key. BCIs can be tailored to individual patient needs, providing uniquely customized feedback and training exercises. Meanwhile, DBS offers consistent stimulation but may not uniquely adapt in real time to the patient’s specific movement or intent.
Striking a balance between current interventions and innovation will heighten treatment outcomes for Parkinson’s disease impairment. This combination could provide a more comprehensive approach to rehabilitation, enabling patients to benefit from the stabilizing effects of DBS while simultaneously gaining real-time feedback and control over assistive devices.
Analysis of Studies on BCI Use for Cognitive Enhancement
The employment of BCI technology in the context of enhancing cognitive functions such as memory, attention, and decision-making in PD patients has garnered significant research interest as well. Research increasingly supports BCIs’ role in enhancing cognitive skills in PD, particularly in areas like attention, decision-making, and memory. For example, a study performed by He et al. (2021) showed that individuals with PD who engaged in non-invasive BCI training consisting of 15 sessions conducted 2–3 times per week over the course of approximately 3 months showed improvements in attention control and decision-making abilities, which were measured based on performance on cognitive tasks. The study used a system that measured attentional engagement by having participants focus on specific stimuli to control a cursor.
Case studies have demonstrated cognitive improvements linked to structured interventions in PD. For example, a Healthy Brain Ageing cognitive training program involving psychoeducation and computer-based cognitive exercises significantly improved learning, memory, and awareness of adaptive cognitive strategies in PD patients. Participants showed medium-to-large effect size gains in memory recall, aligning with the repetitive engagement inherent to such programs (Naismith et al., 2013). Similarly, dual-task training (DTT), which combines cognitive and motor tasks (e.g., walking while solving arithmetic problems), has been shown to enhance both gait performance and cognitive function in PD patients. This approach mirrors the repetitive, task-oriented nature of BCI interventions, fostering neural plasticity and cognitive engagement (Zheng et al., 2021).
Challenges in BCI Integration
Technical Challenges
As we continue to gather data on BCI technologies, several key considerations must be addressed to ensure their effectiveness and safety. One notable point is its coexistence with existing treatments, whether they be pharmacological or physiotherapy (Belkacem et al., 2020). Secondly, it is enhancing the accuracy of signal acquisition, particularly for non-invasive EEG BCIs, which are prone to movement and noise disturbance as well as electrode displacement (Fazel-Rezai et 2012).
Convenience and accessibility are critical in ensuring utmost patient satisfaction when using the technology. “Most BCI applications require calibration data to reverse undesirable changes caused by neural plasticity or micromovements of the electrode arrays. This necessity calls for frequent decoder retraining, an inconvenient and time-consuming process that unnecessarily burdens the user” (Maiseli et al., 2023). As research continues, so has the quest to achieve full autonomy in self-calibration approaches, hence promoting convenience and flexibility.
Ethical Considerations and Affordability
Ethical concerns arise when it comes to BCI implementation, especially for invasive procedures. Many BCI systems require surgical implantation of electrodes, which comes with risks of infection and potential damage to brain tissue. This raises important concerns about informed consent, especially for patients with cognitive impairments who struggle to comprehend everyday conversations and actions. When faced with the decision to undergo a life-changing procedure, they may not be fully aware of the risks and benefits, which may present a challenge for them (Gordon et al., 2024). It’s also critical to consider the potential side effects of BCI technology, such as changes in personality or cognitive function. Since these side effects are not yet fully understood, they add to the ethical challenges and concerns surrounding the use of BCIs.
While providers are continually working to address the issues of accessibility and affordability of BCI technology, these remain significant barriers to widespread adoption. Many BCI technologies are still costly, which limits access for patients in lower socioeconomic groups or in regions with fewer healthcare resources (Fang et al., 2020). This raises an important sociological debate about equity in healthcare, as those who could benefit the most from BCI technology may be left behind due to financial restraints.
Future Directions and Clinical Implementations
As studies and experiments in the field of BCI research continue to grow, they have zoomed in their focus on developing non-invasive techniques and enhancing neuroplasticity through cognitive training. Current studies are also exploring the use of incorporating existing technologies, such as virtual reality (VR) and augmented reality (AR), to enhance the effectiveness of BCIs. These technologies are aimed at creating a more immersive, safe, and welcoming environment, promoting recovery from PD symptoms, and offering a more engaging therapeutic experience (Lockhart et al., 2012). Long-term studies are still needed to fully understand the long-term impact of BCIs on cognitive performance and quality of life (He et al., 2021). Future research should also focus on combining BCI technology with traditional rehabilitation methods and medicinal treatments.
Conclusion
BCIs provide innovative therapeutic solutions that partly restore and enhance physical mobility and cognitive functions, significantly improving the quality of life for individuals with PD.
Cognitive psychology plays a vital role in advancing our understanding of BCIs, particularly in addressing cognitive processes like attention and memory, which are significantly impaired in PD patients. The theory of neuroplasticity is key to this effort, as it underpins the brain’s ability to reorganize and adapt—the mechanism BCI technology seeks to harness. While BCIs serve as the tools facilitating this process, it is the brain’s neuroplastic changes that drive recovery and improvement. Additionally, understanding dual-task interference is crucial, as it serves as a baseline for improvement to better tailor interventions for patients.
Interdisciplinary research is essential to unlocking BCIs’ potential to transform treatment for neurodegenerative diseases. Collaborations between neuroscientists, cognitive psychologists, rehabilitation specialists, and engineers are key to developing effective BCI solutions that are both technically robust and tailored to the unique needs of PD patients.
References
1. Belkacem, A. N., Jamil, N., Palmer, J. A., Ouhbi, S., & Chen, C. (2020). Brain computer interfaces for improving the quality of life of older adults and elderly patients. Frontiers in Neuroscience, 14, 692. https://doi.org/10.3389/fnins.2020.00692https://doi.org/10.3389/fnins.2020.00692
2. Cosgrove, J., & Alty, J. (2018). Cognitive deficits in Parkinson’s disease: Current perspectives. Journal of Parkinsonism and Restless Legs Syndrome, 8, 1–11. https://doi.org/10.2147/JPRLS.S125064https://doi.org/10.2147/JPRLS.S125064
3. Fang, C., Lv, L., Mao, S., Dong, H., & Liu, B. (2020). Cognition deficits in Parkinson’s disease: Mechanisms and treatment. Parkinson’s Disease, 2020, 2076942. https://doi.org/10.1155/2020/2076942https://doi.org/10.1155/2020/2076942
4. Fazel-Rezai, R., Allison, B. Z., Guger, C., Sellers, E. W., Kleih, S. C., & Kübler, A. (2012). P300 brain computer interface: Current challenges and emerging trends. Frontiers in Neuroengineering, 5, 14. https://doi.org/10.3389/fneng.2012.00014https://doi.org/10.3389/fneng.2012.00014
5. Fiorillo, C., De Salvo, S., & Zappia, M. (2019). Deep brain stimulation in Parkinson’s disease: A review. Frontiers in Neuroscience, 13, 53. https://doi.org/10.3389/fnins.2019.00053https://doi.org/10.3389/fnins.2019.00053
6. Gade, G. V., Jørgensen, M. G., Ryg, J., Riis, J., Thomsen, K., Masud, T., & Andersen, S. (2021). Predicting falls in community-dwelling older adults: A systematic review of prognostic models. BMJ Open, 11(5), e044170. https://doi.org/10.1136/bmjopen-2020-044170https://doi.org/10.1136/bmjopen-2020-044170
7. Ghaffari, M., et al. (2019). Neuroanatomy of cognitive dysfunctions in Parkinson’s disease. Neuroscience, 411, 249–265. https://doi.org/10.1016/j.neuroscience.2019.08.006https://doi.org/10.1016/j.neuroscience.2019.08.006
8. Gordon, E. C., & Seth, A. K. (2024). Ethical considerations for the use of brain– computer interfaces for cognitive enhancement. PLOS Biology, 22(10), e3002899. https://doi.org/10.1371/journal.pbio.3002899https://doi.org/10.1371/journal.pbio.3002899
9. He, J., Wang, Y., & Liu, M. (2021). Enhancing cognitive function in Parkinson’s disease through brain-computer interfaces: A systematic review. Computers in Biology and Medicine, 136, 104795. https://doi.org/10.1016/j.compbiomed.2021.104795https://doi.org/10.1016/j.compbiomed.2021.104795
10. Huang, Y., et al. (2019). The impact of bradykinesia and rigidity on the quality of life in patients with Parkinson’s disease. Movement Disorders, 34(8), 1149–1155. https://doi.org/10.1002/mds.27723https://doi.org/10.1002/mds.27723
11. Jellinger, K. A. (2023). Pathobiology of cognitive impairment in Parkinson disease: Challenges and outlooks. International Journal of Molecular Sciences, 25(1), 498. https://doi.org/10.3390/ijms25010498https://doi.org/10.3390/ijms25010498
12. Khezri, M., Jaafar, H., & Zahedi, E. (2022). Survey of machine learning techniques in the analysis of EEG signals for Parkinson’s disease. Applied Sciences, 12(14), 6967. https://doi.org/10.3390/app12146967https://doi.org/10.3390/app12146967
13. Kleim, J. A., & Jones, T. A. (2008). Principles of experience-dependent neural plasticity: Implications for rehabilitation after brain damage. Journal of Speech, Language, and Hearing Research, 51(1), S225–S239. https://doi.org/10.1044/1092-4388(2008/018)https://doi.org/10.1044/1092-4388(2008/018)
14. Kringelbach, M. L., Green, A. L., Owen, S. L. F., Schweder, P. M., & Aziz, T. Z. (2010). Sing the mind electric: Principles of deep brain stimulation. European Journal of Neuroscience, 32(7), 1070–1079. https://doi.org/10.1111/j.1460-9568.2010.07421.xhttps://doi.org/10.1111/j.1460-9568.2010.07421.x
15. Kruse, A., Suica, Z., Taeymans, J., & Schuster-Amft, C. (2020). Effect of brain- computer interface training based on non-invasive electroencephalography using motor imagery on functional recovery after stroke: A systematic review and meta- analysis. BMC Neurology, 20, 1–15. https://doi.org/10.1186/s12883-020-01960-5https://doi.org/10.1186/s12883-020-01960-5
16. Lavermicocca, V., Dellomonaco, A. R., Tedesco, A., Notarnicola, M., Di Fede, R., & Battaglini, P. P. (2018). Neurofeedback in Parkinson’s disease: Technologies in speech and language therapy. Recenti Progressi in Medicina, 109(2), 130–132. https://doi.org/10.1701/2865.28908https://doi.org/10.1701/2865.28908
17. Lebedev, M. A., & Nicolelis, M. A. L. (2006). Brain–machine interfaces: Past, present and future. Trends in Neurosciences, 29(9), 536–546. https://doi.org/10.1016/j.tins.2006.07.004https://doi.org/10.1016/j.tins.2006.07.004
18. Liu, X. Y., Wang, W. L., Liu, M., et al. (2025). Recent applications of EEG-based brain-computer-interface in the medical field. Military Medical Research, 12, 14. https://doi.org/10.1186/s40779-025-00598-zhttps://doi.org/10.1186/s40779-025-00598-z
19. Lockhart, S. N., Mayda, A. B. V., Roach, A. E., Fletcher, E., Carmichael, O., Maillard, P., Schwarz, C. G., Yonelinas, A. P., Ranganath, C., & DeCarli, C. (2012). Episodic memory function is associated with multiple measures of white matter integrity in cognitive aging. Frontiers in Human Neuroscience, 6, 56. https://doi.org/10.3389/fnhum.2012.00056https://doi.org/10.3389/fnhum.2012.00056
20. López-Larraz, E., Sarasola-Sanz, A., Irastorza-Landa, N., Birbaumer, N., & Ramos-Murguialday, A. (2018). Brain-machine interfaces for rehabilitation in stroke: A review. NeuroRehabilitation, 43(1), 77–97. https://doi.org/10.3233/NRE-172408https://doi.org/10.3233/NRE-172408
21. Maiseli, B., Abdalla, A. T., Massawe, L. V., Mbise, M., Mkocha, K., Nassor, N. A., Ismail, M., Michael, J., & Kimambo, S. (2023). Brain-computer interface: Trend, challenges, and threats. Brain Informatics, 10(1), 20. https://doi.org/10.1186/s40708-023-00199-3https://doi.org/10.1186/s40708-023-00199-3
22. Miladinović, A., et al. (2020). Evaluation of motor imagery-based BCI methods in neurorehabilitation of Parkinson’s disease patients. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 3058–3061. https://doi.org/10.1109/EMBC44109.2020.9176651https://doi.org/10.1109/EMBC44109.2020.9176651
23. Moulaison‐Sandy, H. (2023). What is a person? Emerging interpretations of AI authorship and attribution. Proceedings of the Association for Information Science and Technology, 60(1), 279–290. https://doi.org/10.1002/pra2.788https://doi.org/10.1002/pra2.788
24. Naismith, S. L., Mowszowski, L., Diamond, K., & Lewis, S. J. (2013). Improving memory in Parkinson’s disease: A healthy brain ageing cognitive training program. Movement Disorders, 28(8), 1097–1103. https://doi.org/10.1002/mds.25457https://doi.org/10.1002/mds.25457
25. O’Shea, S., Morris, M. E., & Iansek, R. (2002). Dual task interference during gait in people with Parkinson disease: Effects of motor versus cognitive secondary tasks. Physical Therapy, 82(9), 888–897. https://doi.org/10.1093/ptj/82.9.888https://doi.org/10.1093/ptj/82.9.888
26. Rajput, D. R. (1993). Accuracy of clinical diagnosis of idiopathic Parkinson’s disease. Journal of Neurology, Neurosurgery & Psychiatry, 56(8), 938–939. https://doi.org/10.1136/jnnp.56.8.938https://doi.org/10.1136/jnnp.56.8.938
27. Simeral, J. D., Kim, S.-P., Black, M. J., Donoghue, J. P., & Hochberg, L. R. (2011). Neural control of cursor trajectory and click by a human with tetraplegia 1,000 days after implant of an intracortical microelectrode array. Journal of Neural Engineering, 8(2), 025027. https://doi.org/10.1088/1741-2560/8/2/025027https://doi.org/10.1088/1741-2560/8/2/025027
28. Tibrewal, N., Leeuwis, N., & Alimardani, M. (2022). Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users. PLOS ONE, 17(7), e0268880. https://doi.org/10.1371/journal.pone.0268880https://doi.org/10.1371/journal.pone.0268880
29. Tzeng, R. Y., Chiu, P. Y., & Wu, S. Y. (2018). Brain-computer interface and neuroplasticity: An overview. Frontiers in Psychology, 9, 778. https://doi.org/10.3389/fpsyg.2018.00778https://doi.org/10.3389/fpsyg.2018.00778
30. Vidaurre, C., & Blankertz, B. (2010). Towards a cure for BCI illiteracy. Brain Topography, 23(2), 194–198. https://doi.org/10.1007/s10548-009-0121-6https://doi.org/10.1007/s10548-009-0121-6
31. Waldert, S. (2016). Invasive vs. non-invasive neuronal signals for brain–machine interfaces: Will one prevail? Frontiers in Neuroscience, 10, 295. https://doi.org/10.3389/fnins.2016.00295https://doi.org/10.3389/fnins.2016.00295
32. World Health Organization. (2021). Global status report on Parkinson’s disease. https://www.who.int/publications/i/item/9789240057616https://www.who.int/publications/i/item/9789240057616
33. Zheng, Y., Meng, Z., Zhi, X., & Liang, Z. (2021). Dual-task training to improve cognitive impairment and walking function in Parkinson’s disease patients: A brief review. Sports Medicine and Health Science, 3(4), 202–206. https://doi.org/10.1016/j.smhs.2021.10.003https://doi.org/10.1016/j.smhs.2021.10.003