Estimating the hurst exponent in motor imagery-based brain computer interface

The objective of this paper is to detect sensorimotor rhythms (mu and beta) produced by right and left hand motor imagery. The electroencephalographic (EEG) data were recorded with 8 g.tec active electrodes by means of g.MOBIlab+ module. The EEG data are wavelet multiresolution decomposed into subbands of interest (7.5-15 Hz-mu rhythm, 15-30Hz-beta rhythm). We applied absolute moment and aggregated variance methods to estimate the Hurst exponent of these decomposed signals, with different types of wavelet. We obtained very good discrimination on channels C3 and CP3 for right hand motor imagery signal and on channels C4 and CP4, when left hand was imaginarily moved. The subjects discriminated better the beta rhythm.