PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Generative Independent Component Analysis for EEG Classification
Silvia Chiappa and David Barber
In: ESANN 2005, 27-29 April 2005, Bruges, Belgium.

Abstract

We present an application of Independent Component Analysis (ICA) to the discrimination of mental tasks for EEG-based Brain Computer Interface systems. ICA is most commonly used with EEG for artifact identification with little work on the use of ICA for direct discrimination of different types of EEG signals. By viewing ICA as a generative model, we can use Bayes' rule to form a classifier. This enables us also to investigate whether simple spatial information is sufficiently informative to produce state-of-the-art results when compared to more traditional methods based on using temporal features as inputs to off-the-shelf classifiers. Experiments conducted on two subjects suggest that knowing `where' activity is happening alone gives encouraging results.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:1985
Deposited By:Silvia Chiappa
Deposited On:09 January 2006