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In brain machine interfaces (BMI) that are used to control motorrehabilitation devices there is the need to process the monitored brain signalswith the purpose of recognizing patient's intentions to move his hands or limbsand reject artifact and noise superimposed on these signals. This kind ofprocessing has to take place within time limits imposed by the on-line controlrequirements of such devices. A widely-used algorithm is the Second Order BlindIdentification (SOBI) independent component analysis (ICA) algorithm. Thisalgorithm, however, presents long processing time and therefor it not suitablefor use in the brain-based control of rehabilitation devices. A rework of thisalgorithm that is presented in this paper and based on SCHUR decompositionresults to significantly reduced processing time. This new algorithm is quiteappropriate for use in brain-based control of rehabilitation devices.

A reworked SOBI algorithm based on SCHUR Decomposition for EEG data processing