Identification of EEG features during motor imagery by heuristic BCI with L-FTM, after learning of EEG features during exercise.

Abstract

It is known that EEG drastically changes depending on external influences and health condition of an experimental participant. BCI based on the focused features of EEG signal, such as frequency band or measurement sites is suitable only for the users with reproducible, major EEG features evoked by a certain cognitive task. To avoid this problem, we developed a BCI using Learning-type-Fuzzy-Template-Matching (L-FTM) method. In addition, we implemented pruning procedure that deletes unsuitable fuzzy rules with high compatibility degree to both of task and non-task status. We confirmed that was BCI system detected the EEG features of a participant during imaging movement task, after learning the EEG features accompanied by motor task.