Classifier of Motor EEG Images for Real Time BCI

The work is devoted to the development of a classifier of motor activity patterns based on electroencephalograms (EEG) for a real-time brain-computer interface (BCI), which can be used in contactless control systems. Conducted studies of various methods for classifying motor EEG images have shown that their effectiveness significantly depends on the implementation of the stages of information processing in the BCI. The most effective classification method turned out to be the support vector machine. However, its long operating time and lack of accuracy make it difficult to use for implementing real-time BCI. Therefore, a classifier was developed using an ensemble of detectors, each of which is trained to recognize its own motor EEG image. A new EEG analysis algorithm based on event functions was applied. A study of the classifier showed that it is possible to achieve detection accuracy of 98.5% with an interface delay of 230 ms.