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Classification of movement intention by spatially filtered electromagnetic inverse solutions

Marco Congedo 1, * Fabien Lotte 2 Anatole Lécuyer 2
* Corresponding author
2 BUNRAKU - Perception, decision and action of real and virtual humans in virtual environments and impact on real environments
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, ENS Cachan - École normale supérieure - Cachan, Inria Rennes – Bretagne Atlantique
Abstract : We couple the standardized low-resolution electromagnetic tomography (sLORETA), an inverse solution for electroencephalography (EEG) and the common spatial pattern, which is here conceived as a data-driven beamformer, to classify the benchmark BCI (Brain Computer Interface) competition 2003, data set IV. The data-set is from an experiment where a subject performed a selfpaced left and right finger tapping task. Available for analysis are 314 training trials whereas 100 unlabeled test trials have to be classified. The EEG data from 28 electrodes comprise the recording of the 500 ms before the actual finger movements, hence represents uniquely the left and right finger movement intention. Despite our use of an untrained classifier, and we extract only one attribute per class, our method yields accuracy similar to the winners of the competition for this data-set. The distinct advantages of the approach presented here are the use of an untrained classifier and the processing speed, which make the method suitable for actual BCI applications. The proposed method is favourable over existing classification methods based on EEG inverse solution, which either rely on iterative algorithms for single-trial independent component analysis or on trained classifiers.
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Submitted on : Tuesday, March 6, 2007 - 9:22:21 AM
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Marco Congedo, Fabien Lotte, Anatole Lécuyer. Classification of movement intention by spatially filtered electromagnetic inverse solutions. Physics in Medicine and Biology, IOP Publishing, 2006, 51, pp.1971-1989. ⟨10.1088/0031-9155/51/8/002⟩. ⟨inria-00134948⟩



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