| Abstrakt: |
Electroencephalogram (EEG)-based brain–computer interface (BCI) system is a promising tool for personalized rehabilitation post-stroke. Previous research has demonstrated the fundamental elements of these systems, including efficient classification, validated source localization, and visualization of EEG from stroke survivors. However, little attention has been given to developing a holistic framework to employ these elements in a human-in-the-loop personalized rehabilitation system. Without a holistic examination and development, we undervalue the context of personalized rehabilitation, ultimately hindering the agreement and acceptability in clinical practices and patients’ preferences. Therefore, this study proposed a holistic computational pipeline to employ classification, source localization, and visualization of EEG for personalized rehabilitation of stroke survivors. To this end, we designed an experiment focusing on upper limb movement in which a participant voluntarily performed the left hand’s shoulder, elbow, and wrist movements several times while simultaneously brain data were recorded with an EEG cap. Based on the recorded EEG data, we first developed feature engineering, importance analysis, and machine learning approaches with considerations of real-time implementations. EEG source localization was performed using the sLORETA method in Brainstorm to illustrate consistency for enabling agreement upon clinical conclusion about the brain areas that have been repeatedly activated when performing each of these movements. The experimental results demonstrated that decision trees of optimal selected EEG-channel features could achieve the best classification performance (95.63% Accuracy, 0.89 AUC). Furthermore, the EEG channels chosen by the decision trees showed consistency with the source localization. |
Full Text from ScienceDirect