PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Generative Temporal ICA for Classification in Asynchronous BCI Systems
Silvia Chiappa and David Barber
In: 2nd International IEEE EMBS Conference On Neural Engineering, 16-19 March 2005, Washington DC, USA.


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 ICA 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-the-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 components with an autoregressive process in order to investigate whether temporal information can bring any advantage in terms of discrimination of spontaneous mental tasks.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:1986
Deposited By:Silvia Chiappa
Deposited On:09 January 2006