Vavoulis, A.; Figueiredo, P.; Vourvopoulos, A. A Review of Online Classification Performance in Motor Imagery-Based Brain–Computer Interfaces for Stroke Neurorehabilitation. Signals2023, 4, 73-86.
Vavoulis, A.; Figueiredo, P.; Vourvopoulos, A. A Review of Online Classification Performance in Motor Imagery-Based Brain–Computer Interfaces for Stroke Neurorehabilitation. Signals 2023, 4, 73-86.
Vavoulis, A.; Figueiredo, P.; Vourvopoulos, A. A Review of Online Classification Performance in Motor Imagery-Based Brain–Computer Interfaces for Stroke Neurorehabilitation. Signals2023, 4, 73-86.
Vavoulis, A.; Figueiredo, P.; Vourvopoulos, A. A Review of Online Classification Performance in Motor Imagery-Based Brain–Computer Interfaces for Stroke Neurorehabilitation. Signals 2023, 4, 73-86.
Abstract
Motor imagery(MI)-based Brain-Computer Interfaces (BCI) have shown increased potential for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice has been restricted due to their low accuracy performance. To date, although a lot of research has been made in benchmarking and highlighting the most valuable classification algorithms in BCI configurations, most of them are using offline data and not from real BCI performance during the closed-loop (or online) sessions. Since rehabilitation training relies on the availability of an accurate feedback system, we surveyed articles of current and past EEG-based BCI frameworks who report the online classification of the movement of two upper limbs in both healthy volunteers and stroke patients. We found that the recently developed Deep Learning methods do not outperform the traditional machine learning algorithms. In addition, patients and healthy subjects exhibit similar classification accuracy in current BCI configurations. Lastly, in terms of neurofeedback modality, Functional Electrical Stimulation (FES) yielded the best performance compared to non-FES systems.
Keywords
Brain-Computer Interfaces; Electroencephalogram; Motor-Imagery; Machine Learning; Deep Learning; Classification; Neurorehabilitation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
