基于SCHUR分解的改进型SOBI脑电数据处理算法

Gregory A Kalogiannis, Karampelas Nikolaos, G. Hassapis
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引用次数: 5

摘要

在用于控制运动康复设备的脑机接口(BMI)中,需要处理监测到的大脑信号,以识别患者移动其手或四肢的意图,并排除叠加在这些信号上的伪影和噪声。这种处理必须在此类设备的在线控制要求所规定的时间限制内进行。一种广泛使用的算法是二阶盲识别(SOBI)独立分量分析(ICA)算法。但该算法处理时间长,不适合用于康复设备的脑控。本文提出了一种基于SCHUR分解结果的改进算法,以显著缩短处理时间。这种新算法非常适合用于基于大脑的康复设备控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Reworked SOBI Algorithm Based on SCHUR Decomposition for EEG Data Processing
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 patients 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.
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