Classification methods for ongoing EEG and MEG signals.
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 quantitative functional variables used in this classifier, and of the validation method. In addition, our findings suggest that by investigating the distribution of classifier coefficients, it is possible to infer knowledge and construct functional interpretations of the underlying neural mechanisms of the performed tasks. Finally, the promising results reported here (up to 97% classification accuracy on 1-second time windows) reflect the considerable potential of MEG for the continuous classification of mental states.
CNRS, UPR 640-LENA, Laboratoire Meurosciencies Cognitives el Imagerie Cérébrale, 75013 Paris Cedex 13, France. besserve@chups.jussieu.fr
Michel
M
Besserve
Karim
K
Jerbi
Francois
F
Laurent
Sylvain
S
Baillet
Jacques
J
Martinerie
Line
L
Garnero
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000145:classification*; D006801:Humans; D016014:Linear Models; D015225:Magnetoencephalography / Q000145:classification*; D009043:Motor Activity / Q000502:physiology*; D010364:Pattern Recognition, Visual / Q000502:physiology*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted