Single-trial classification of elementary comparison processes on the basis of instantaneous EEG and MEG coherences.
The present study is focused on the evidence of possible single-trial EEG/MEG analysis of information processing. The discrimination between thinking modalities of concept activation and pattern comparison for single tasks of elementary comparison procedures is investigated. A neural network classifier with backpropagation learning algorithm is used. The input vector is constructed by parameters of instantaneous coherence (13-20 Hz) between several channel pairs of the EEG and/or of the MEG. Thereby, the strength of synchronization and the time location of synchronization phenomena are taken into consideration. The combination of EEG and MEG coherence parameters led to a classification accuracy of 85-94% for single subjects. Generally, results reached by neural network classifier show a better generalization than linear discriminant analysis.
Steuer
Dunja
D
Department of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Germany. Dunja.Steuer@tu-ilmenau.de
Grieszbach
Gert
G
Krause
Werner
W
Schack
Bärbel
B
D000328:Adult; D000465:Algorithms; D002965:Classification / Q000379:methods*; D003071:Cognition; D003210:Concept Formation; D016002:Discriminant Analysis; D004569:Electroencephalography; D001330:Electronic Data Processing / Q000379:methods*; D007839:Functional Laterality; D006801:Humans; D015225:Magnetoencephalography; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010364:Pattern Recognition, Visual; D011930:Reaction Time; D012815:Signal Processing, Computer-Assisted