This paper proposes a method for highlighting the characteristics of sensorimotor rhythms (mu and beta). The electroencephalographic (EEG) data were recorded with 8 g.tec active electrodes by means of g.MOBIlab+ module. The EEG signals were filtered with a fifth order Butterworth band-pass filter between 0 and 30Hz and then the independent component analysis (ICA) was applied. The coefficient of determination (r 2 ) has been computed for both situations, comparing the EEG spectra associated with each motor-imagery task with the spectra recorded in resting conditions. ICA and the coefficient of determination help us to demonstrate that the recorded data can be used to implement a brain computer interface (BCI) based on motor imagery tasks. Imagining left hand movement produces a desynchronization on CP4 and C4 electrodes in the right side of the scalp, while imagining right hand movement produces a desynchronization on CP3, C3 and P3 electrodes, on the left side of the brain.

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Abstract-In this paper, we address a method for motor imagery feature extraction for brain computer interface (BCI). The wavelet coefficients were used to extract the features from the motor imagery EEG and the linear discriminant analysis was utilized to classify the pattern of left or right hand imagery movement and rest. The performance of the proposed method was evaluated using EEG data recorded by us, with 8 g.tec active electrodes by means of g.MOBIlab+ module. The maximum accuracy of classification is 91%.

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.

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