article i nfo We propose a new data-driven decoding method called Spectral Linear Discriminant Analysis (Spectral LDA) for the analysis of magnetoencephalography (MEG). The method allows investigation of changes in rhythmicneural activity as a result of different stimuli and tasks. The introduced classification model only assumes that each "brain state" can be characterized as a combination of neural sources, each of which shows rhythmic activity at one or several frequency bands. Furthermore, the model allows the oscillation frequencies to be different for each such state. We present decoding results from 9 subjects in a four-category classification problem defined by an experiment involving randomly alternating epochs of auditory, visual and tactile stimuli interspersed with restperiods. The performance of Spectral LDA was very competitivecomparedwith four alternative classifiers based on different assumptions concerning the organizationof rhythmic brainactivity. Inaddition, the spectral and spatial patterns extracted automatically on the basis of trained classifiers showed that Spectral LDA offers a novel and interesting way of analyzing spectrospatial oscillatory neural activity across the brain. All the presented classification methods and visualization tools are freely available as aM atlab toolbox.
['Jukka-Pekka Kauppi', 'Lauri Parkkonen', 'Riitta Hari', 'Aapo Hyvärinen']
Decoding magnetoencephalographic rhythmic activity using spectrospatial information