Generative Independent Component Analysis for EEG Classification

Silvia Chiappa
2005
Article de conférence

Résumé

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.

Official source

https://infoscience.epfl.ch/record/83255?ln=fr

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Chargement

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. We fit spatial filters and source distribution parameters simultaneously and investigate whether these are sufficiently informative to produce good results when compared to more traditional methods based on using temporal features as inputs to off-the-shelf classifiers. Experiments suggest that state-of-the-art results may indeed be found without explicitly using temporal features. We extend the method to using a mixture of ICA models, consistent with the assumption that subjects may have more than one approach to thinking about a specific mental task.

IDIAP2004

Chargement

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. We fit spatial filters and source distribution parameters simultaneously and investigate whether these are sufficiently informative to produce good results when compared to more traditional methods based on using temporal features as inputs to off-the-shelf classifiers. Experiments suggest that state-of-the-art results may indeed be found without explicitly using temporal features. We extend the method to using a mixture of ICA models, consistent with the assumption that subjects may have more than one approach to thinking about a specific mental task.

2006

Chargement

In this paper we investigate the use of a temporal extension of Independent Component Analysis (ICA) for the discrimination of three mental tasks for asynchronous EEG-based Brain Computer Interface systems. ICA is most commonly used with EEG for artifact identification with little work on the use of I CA for direct discrimination of different types of EEG signals. In a recent work we have shown that, by viewing ICA as a generative model, we can use Bayes' rule to form a classifier obtaining state-of-the-art results when compared to more traditional methods based on using temporal features as inputs to off-th e-shelf classifiers. However, in that model no assumption on the temporal nature of the independent components was made. In this work we model the hidden co mponents with an autoregressive process in order to investigate whether temporal information can bring any advantage in terms of discrimination of spontaneo us mental tasks.

IDIAP2005

Chargement