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| BESSERVE,MICHEL; JERBI,KARIM; LAURENT,FRANCOIS; BAILLET,SYLVAIN; MARTINERIE,JACQUES; GARNERO,LINE. |
| Classification algorithms help predict the qualitative properties of a subject's mental state by extracting useful information from the highly multivariate non-invasive recordings of his brain activity. In particular, applying them to Magneto-encephalography (MEG) and electro-encephalography (EEG) is a challenging and promising task with prominent practical applications to e.g. Brain Computer Interface (BCI). In this paper, we first review the principles of the major classification techniques and discuss their application to MEG and EEG data classification. Next, we investigate the behavior of classification methods using real data recorded during a MEG visuomotor experiment. In particular, we study the influence of the classification algorithm, of the... |
| Tipo: Journal article |
Palavras-chave: Brain computer interface; Electroencephalography; Magnetoencephalography; Visuomotor control; Support Vector Machine. |
| Ano: 2007 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-97602007000500005 |
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