Abstract:
:We couple standardized low-resolution electromagnetic tomography, 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 self-paced left and right finger tapping task. Available for analysis are 314 training trials whereas 100 unlabelled 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 represent uniquely the left and right finger movement intention. Despite our use of an untrained classifier, and our extraction of 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 an EEG inverse solution, which rely either on iterative algorithms for single-trial independent component analysis or on trained classifiers.
journal_name
Phys Med Bioljournal_title
Physics in medicine and biologyauthors
Congedo M,Lotte F,Lécuyer Adoi
10.1088/0031-9155/51/8/002subject
Has Abstractpub_date
2006-04-21 00:00:00pages
1971-89issue
8eissn
0031-9155issn
1361-6560pii
S0031-9155(06)04845-7journal_volume
51pub_type
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