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Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time–frequency tilings
2006
Journal of Neural Engineering
We describe a new technique for the classification of motor imagery electroencephalogram (EEG) recordings in a brain computer interface (BCI) task. The technique is based on an adaptive time-frequency analysis of EEG signals computed using local discriminant bases (LDB) derived from local cosine packets (LCP). In an offline step, the EEG data obtained from the C 3 /C 4 electrode locations of the standard 10/20 system is adaptively segmented in time, over a non-dyadic grid by maximizing the
doi:10.1088/1741-2560/3/3/006
pmid:16921207
fatcat:r3yjzgxavbavxnlshd3gyyc2cm