Development of the Heuristic BCI System with Learning-Type Fuzzy Template Matching Method

Abstract

Non-invasive brain-computer interface (BCI) based on electroencephalogram (EEG) is generally limited to specific measurement sites and frequency bands. These types of BCI systems utilize certain target EEG features evoked by cognitive tasks and detects the certain EEG features to determine BCI control. However, it seems that the user who well reproduce these EEG features are suitable for such EEG-BCI, but often the BCI is not suitable for other users without reproducible EEG features evoked by the same task. Then we propose a heuristic BCI system without a priori assumption of focused EEG features for detection. In this system, measurement sites and frequency bands are not selected in advance, but the BCI system searches the suitable measurement sites and frequency bands suitable for reproducible EEG features for the user, by learning process. Learning-type Fuzzy Template Matching method (L-FTM) based on simplified fuzzy logic with learning is used for the heuristic algorithm. We succeeded in extracting the EEG features evoked during right-upper limb motor imagery task, using the developed BCI system. We also confirmed that using the heuristic-BCI system, with fuzzy rules corresponding to specific EEG feature patterns were automatically extracted by the learning process. Thus, the developed BCI system was suggested that it learns and extracts EEG features corresponding to a certain task without prior information. The BCI system can also be used as an effective tool for detecting EEG features.