  In brain machine interfaces (BMI) that are used to control motor
rehabilitation devices there is the need to process the monitored brain signals
with the purpose of recognizing patient's intentions to move his hands or limbs
and reject artifact and noise superimposed on these signals. This kind of
processing has to take place within time limits imposed by the on-line control
requirements of such devices. A widely-used algorithm is the Second Order Blind
Identification (SOBI) independent component analysis (ICA) algorithm. This
algorithm, however, presents long processing time and therefor it not suitable
for use in the brain-based control of rehabilitation devices. A rework of this
algorithm that is presented in this paper and based on SCHUR decomposition
results to significantly reduced processing time. This new algorithm is quite
appropriate for use in brain-based control of rehabilitation devices.
