Selective Subband Entropy for Motor Imagery Detection in Asynchronous Brain Computer Interface

The motor imagery detection is a very important problem in the asynchronous control for direct brain computer interface. To address this issue, this paper proposes a novel detection method based on subband entropy analysis in a selected frequency band. The basic idea of this method is that, in some specific frequency band, the complexity (or randomness) of brain signal during the stage of concentrating on the motor imagery is lower than that of free thinking. Once the optimal frequency band is selected, the subband entropy $an indicator of complexity and randomness - can be used for detecting the motor imagery. In this work, we develop the method using only one dipolar EEG channel. Furthermore, we propose a system calibration method based on an empirical measurement what we refer as unsupervised discriminative index (UDI). The proposed calibration method is rapid and able to avoid a typical problem of asynchronous BCI training that is the correct labeling of continuous EEG signal. The proposed method not only improve the accuracy of the detection but free from parameter tweaking. The experiment conducted on three different subjects shows advantage of the proposed method over the conventional framework based on fixed-band filter and energy feature. A detection accuracy up to 77% at false positive rate of 2% was obtained without any subject training.