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Computational Intelligence and Neuroscience
Volume 2007 (2007), Article ID 41468, 9 pages
Research Article

Extracting Rhythmic Brain Activity for Brain-Computer Interfacing through Constrained Independent Component Analysis

Signal Processing and Control Group, ISVR, University of Southampton, Southampton SO17 1BJ, UK

Received 31 December 2006; Accepted 18 June 2007

Academic Editor: Andrzej Cichocki

Copyright © 2007 Suogang Wang and Christopher J. James. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose a technique based on independent component analysis (ICA) with constraints, applied to the rhythmic electroencephalographic (EEG) data recorded from a brain-computer interfacing (BCI) system. ICA is a technique that can decompose the recorded EEG into its underlying independent components and in BCI involving motor imagery, the aim is to isolate rhythmic activity over the sensorimotor cortex. We demonstrate that, through the technique of spectrally constrained ICA, we can learn a spatial filter suited to each individual EEG recording. This can effectively extract discriminatory information from two types of single-trial EEG data. Through the use of the ICA algorithm, the classification accuracy is improved by about 25%, on average, compared to the performance on the unpreprocessed data. This implies that this ICA technique can be reliably used to identify and extract BCI-related rhythmic activity underlying the recordings where a particular filter is learned for each subject. The high classification rate and low computational cost make it a promising algorithm for application to an online BCI system.