H/sup /spl infin// adaptive filters for eye blink artifact minimization from electroencephalogram

Two adaptive algorithms (time varying and exponentially weighted) based on the H/sup /spl infin// principles are proposed for the minimization of electrooculogram (EOG) artifacts from corrupted electroencephalographic signals. Performance of the proposed algorithms are compared with the least-mean-square (LMS) algorithm. Improvements in the output signal-to-noise ratio along with time plots are used for the comparison. It is found that the H/sup /spl infin//-based algorithms effectively minimize the EOG artifacts and always outperform the LMS algorithm.