Electroencephalography-Based Brain-Computer Interfaces for Parkinson?s Motor Rehabilitation: A Review of Cognitive Fatigue Challenges
Abstract: Parkinson's disease (PD) is a progressive neurodegenerative disorder characterised by motor impairments such as tremor, rigidity, and bradykinesia, which reduces quality of life. EEG-based brain computer interfaces (BCIs) have emerged as a promising adjunct for motor rehabilitation by translating neural activity, including β and μ rhythms, into control signals that support neuroplasticity. This review examines key EEG signals used in BCI systems, including event-related desynchronization and synchronisation, and evaluates their reliability in PD. Particular attention is given to cognitive fatigue, which alters neural dynamics, reduces signal-to-noise ratio, and decreases classification accuracy across sessions. The review synthesises current BCI modalities and highlights challenges related to inter-session variability and sustained cognitive demand. Adaptive strategies such as fatigue-aware system design, AI-driven classifiers, hybrid EEG-EMG and EEG-VR systems, and wearable home-based platforms are discussed as potential solutions. Overall, EEG-based BCIs show strong potential for PD rehabilitation, but addressing cognitive fatigue and signal instability remains critical for long-term clinical application.
Keywords: Parkinson's disease, EEG, brain computer interface, cognitive fatigue, motor imagery, event-related desynchronization, neurorehabilitation, machine learning
How to Cite?: Naisha Shashidhar, Dr Soumya Ravikumar, "Electroencephalography-Based Brain-Computer Interfaces for Parkinson?s Motor Rehabilitation: A Review of Cognitive Fatigue Challenges", Volume 15 Issue 4, April 2026, International Journal of Science and Research (IJSR), Pages: 842-848, https://www.ijsr.net/getabstract.php?paperid=SR26408120651, DOI: https://dx.doi.org/10.21275/SR26408120651