Brain computer interface design using band powers extracted during mental tasks

In this paper, a Brain Computer Interface (BCI) is designed using electroencephalogram (EEG) signals where the subjects have to think of only a single mental task. The method uses spectral power and power difference in 4 bands: delta and theta, beta, alpha and gamma. This could be used as an alternative to the existing BCI designs that require classification of several mental tasks. In addition, an attempt is made to show that different subjects require different mental task for minimising the error in BCI output. In the experimental study, EEG signals were recorded from 4 subjects while they were thinking of 4 different mental tasks. Combinations of resting (baseline) state and another mental task are studied at a time for each subject. Spectral powers in the 4 bands from 6 channels are computed using the energy of the Elliptic FIR filter output. The mental tasks are detected by a neural network classifier. The results show that classification accuracy up to 97.5% is possible, provided that the most suitable mental task is used. As an application, the proposed method could be used to move a cursor on the screen. If cursor movement is used with a translation scheme like Morse Code, the subjects could use the proposed BCI for constructing letters/words. This would be very useful for paralysed individuals to communicate with their external surroundings