Non-supervised technique to adapt spatial filters for ECoG data analysis

Electrical Brain signals can be used for developing non-muscular communication and control systems, Brain-Computer Interfaces (BCIs) for people with motor disabilities. The performance of a BCI relies on the measured components of the brain activity, and on the feature extraction achieved by the spatial and temporal filtering methods applied prior to its translation into commands. In the present study we proposed a non-supervised technique based on the steepest descent method with a minimization cost function given by the variance on differences of the linear combination of the electrodes in order to adapt filter´s coefficients to the most appropriate spatial filter. Results of applying this technique to electrocorticographic (ECoG) signals of five subjects performing finger flexion task are shown. Adapted filters were compared with Common Average Reference Filter (CAR) when mean square error (MSE) between channels significantly correlated and the power of filtered data was computed; results proved that adapted filters have better performance. Paired t-test was conducted to prove that results from CAR and the proposed technique are significantly different.