Publication

Generative Temporal ICA for Classification in Asynchronous BCI Systems

2005
Reports

Abstract

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.

Official source

https://infoscience.epfl.ch/entities/publication/fbe29b83-0420-4163-802b-974297dd5745

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2005
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/fbe29b83-0420-4163-802b-974297dd5745

About this result

Related concepts (9)

Independent component analysis

In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that at most one subcomponent is Gaussian and that the subcomponents are statistically independent from each other. ICA is a special case of blind source separation. A common example application is the "cocktail party problem" of listening in on one person's speech in a noisy room.

Principal component analysis

Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data.

Signal separation

Source separation, blind signal separation (BSS) or blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process. It is most commonly applied in digital signal processing and involves the analysis of mixtures of signals; the objective is to recover the original component signals from a mixture signal.