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Rehabilitation through Brain Computer Interfaces - Classification and Feedback Study

A Brain-Computer Interface (BCI) is a tool for reading and interpreting signals recorded directly from the user’s brain. Most brain-computer interfaces (BCI) are based on one of three types of electroencephalogram (EEG) signals: P300s, steady-state visually evoked potentials (SSVEPs), and event-related desynchronization (ERD). EEG is typically recorded non-invasively using active or passive electrodes mounted on the human scalp. In recent years, a variety of different BCIs for communication and control applications were developed. A quite new and promising idea is to utilize BCIs as a tool for stroke rehabilitation. The BCI detects the user's movement intention and provides online feedback to train the affected parts of the body to restore effective movement. This publication tries to optimize current BCIstrategies for stroke rehabilitation using immersive 3-D virtual reality feedback (VRFB). Other work has continued to show that higher density electrode systems can reveal subtleties of brain dynamics that are not obvious with fewer electrodes. Hence, we used a larger electrode montage than typical BCI studies.