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

TL;DR

This paper introduces a modified SOBI algorithm utilizing SCHUR decomposition to significantly reduce processing time, making it suitable for real-time brain-machine interface applications in motor rehabilitation.

Contribution

A novel SOBI algorithm based on SCHUR decomposition that enhances processing speed for EEG signal analysis in brain-machine interfaces.

Findings

Reduced processing time compared to standard SOBI

Suitable for real-time EEG processing in rehabilitation devices

Maintains accuracy in signal separation

Abstract

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.