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 [BibTeX] [Marc21]
Generative Independent Component Analysis for EEG Classification
Type of publication: Conference paper
Citation: silviac04generative
Booktitle: European Symposium on Artificial Neural Networks ESANN
Year: 2005
Note: IDIAP-RR 04-77
Crossref: silviac-rr04-77:
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.
Userfields: ipdmembership={learning},
Projects Idiap
Authors Chiappa, Silvia
Barber, David
Added by: [UNK]
Total mark: 0
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