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Article Dans Une Revue IEEE Transactions on Biomedical Engineering Année : 2010

Non-Stationary Brain Source Separation for Multi-Class Motor Imagery

Résumé

This article describes a method to recover taskrelated brain sources in the context of multi-class Brain- Computer Interfaces (BCIs) based on non-invasive electroencephalography (EEG). We extend the method Joint Approximate Diagonalization (JAD) for spatial filtering using a maximum likelihood framework. This generic formulation (1) bridges the gap between the Common Spatial Patterns (CSP) and Blind Source Separation (BSS) of non-stationary sources, and (2) leads to a neurophysiologically adapted version of JAD, accounting for the successive activations/deactivations of brain sources during motor imagery trials. Using dataset 2a of BCI Competition IV (2008) in which nine subjects were involved in a four-class two-session motorimagery (MI) based BCI experiment, a quantitative evaluation of our extension is provided by comparing its performance against JAD and CSP in the case of cross-validation as well as session-to-session transfer. Whereas JAD, as already proposed in other works, does not prove to be significantly better than classical one-versus-rest CSP, our extension is shown to perform significantly better than CSP for cross-validated and session-to-session performance. The extension of JAD introduced in this paper yields among the best session-to-session transfer results presented so far for this particular dataset, thus it appears of great interest for real-life BCIs.
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Dates et versions

hal-00460494 , version 1 (02-03-2010)

Identifiants

Citer

Cedric Gouy-Pailler, Marco Congedo, Clemens Brunner, Christian Jutten, Gert Pfurtscheller. Non-Stationary Brain Source Separation for Multi-Class Motor Imagery. IEEE Transactions on Biomedical Engineering, 2010, 57 (2), pp.469-478. ⟨10.1109/TBME.2009.2032162⟩. ⟨hal-00460494⟩
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