Separation and Recognition of Electroencephalogram Patterns Using Temporal Independent Component Analysis

摘要

A common problem in Electroencephalogram (EEG) analysis is how to separate EEG patterns from noisy recordings. Independent component analysis (ICA), which is an effective method to recover independent sources from sensor outputs without assuming any a priori knowledge, has been widely used in such biological signals analysis. However, when dealing with EEG signals, the mixing model usually does not satisfy the standard ICA assumptions due to the time-variable structures of source signals. In this case, EEG patterns should be precisely separated and recognized in a short time window. Another issue is that we usually over-separate the signals by ICA due to the over learning problem when the length of data is not sufficient. In order to tackle these problems mentioned above, we try to exploit both high order statistics and temporal structures of source signals under condition of short time windows. We utilize a temporal-independent component analysis (tICA) method to formulate the blind separation problem into a new framework of analyzing the mutual independence of the residual signals. Furthermore, in order to find better features for classification, both temporal and spatial features of EEG recordings are extracted by integrating tICA together with some other algorithm like Common Spatial Pattern (CSP) for feature extraction. Computer simulations are given to evaluate the efficiency and performance of tICA based on EEG data recorded not from the normal people but from some special populations suffering from neurophysiological diseases like stroke. To the best of our knowledge, this is the first time that EEG characteristics of stroke patients are explored and reported using ICA algorithm. Superior separation performance and high classification rate evidence that the tICA method is promising for EEG analysis.