Lin Xueyi, Wang Lu, Ohtsuki Tomoaki
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:502-505. doi: 10.1109/EMBC44109.2020.9176484.
Electroencephalogram (EEG) signals are important to study the activities of human brains. The independent component analysis (ICA) algorithm is a practical blind source separation (BSS) technique that can separate EEG sources from artifacts effectively. However, most traditional ICA algorithms assume that the mixing process is instantaneous and off-line. In this paper, a novel framework based on the extension of the online recursive ICA algorithm (ORICA) is proposed to apply for motor imagery (MI) EEG recording. The contributions are as follows. Firstly, we show ORICA's adaptability to accurate and effective source separation used for artifact-contaminated MI EEG recording. Secondly, to identify EOG signals on the output of source separation, the topographic map is presented to distinguish the target signals. The experimental results show that the proposed framework is able to be applied to process MI EEG recording in real-time situations.
脑电图（EEG）信号对于研究人类大脑活动至关重要。独立成分分析（ICA）算法是一种实用的盲源分离（BSS）技术，能够有效地从伪迹中分离出EEG源。然而，大多数传统的ICA算法都假设混合过程是瞬时且离线的。本文提出了一种基于在线递归ICA算法（ORICA）扩展的新颖框架，用于运动想象（MI）EEG记录。贡献如下。首先，我们展示了ORICA在用于受伪迹污染的MI EEG记录的准确有效源分离方面的适应性。其次，为了识别源分离输出上的眼电（EOG）信号，呈现地形图以区分目标信号。实验结果表明，所提出的框架能够应用于实时处理MI EEG记录。
