A reworked SOBI algorithm based on SCHUR Decomposition for EEG data processing
This work addresses the need for faster signal processing in brain-machine interfaces for motor rehabilitation, enabling real-time control, though it is incremental as it modifies an existing algorithm.
The paper tackles the problem of long processing time in the Second Order Blind Identification (SOBI) algorithm for EEG data processing in brain-machine interfaces, resulting in a reworked algorithm based on SCHUR decomposition that significantly reduces processing time, making it suitable for real-time control of rehabilitation devices.
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.