PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Time-Dependent Demixing of Task-Relevant EEG Signals
Jeremy Hill, Jason Farquhar, Thomas Navin Lal and Bernhard Schölkopf
In: 3rd International Brain-Computer Interface Workshop and Training Course, 14-15 Aug 2006, Graz, Austria.

Abstract

Given a spatial filtering algorithm that has allowed us to identify task-relevant EEG sources, we present a simple approach for monitoring the activity of these sources while remaining relatively robust to changes in other (task-irrelevant) brain activity. The idea is to keep spatial *patterns* fixed rather than spatial filters, when transferring from training to test sessions or from one time window to another. We show that a fixed spatial pattern (FSP) approach, using a moving-window estimate of signal covariances, can be more robust to non-stationarity than a fixed spatial filter (FSF) approach.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:2710
Deposited By:Jason Farquhar
Deposited On:22 November 2006