A FastICA-based Approach to Extracting P300 Potential

From two aspects, this paper analyzes the application of fast independent component analysis (FastICA) algorithm in the extraction of P300 by a few trials averaging, and presents simulation results for both normal and disabled subjects. Firstly, the noises are removed from the observed signals by using FastICA, and a few P300 trials are averaged. The features of P300 for both normal and disabled subjects are analyzed. Secondly, in terms of pattern recognition, the number of trials is reduced successively, and the difficulties of distinguishing targets stimuli from non-target ones based on the features are analyzed. The results illustrate the validity of FastICA in the application of the fast extraction of P300.