TY - JOUR
T1 - Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns
AU - Wang, Tao
AU - Deng, Jie
AU - He, Bin
PY - 2004/12/1
Y1 - 2004/12/1
N2 - To develop a single trial motor imagery (MI) classification strategy for the brain-computer interface (BCI) applications by using time-frequency synthesis approach to accommodate the individual difference, and using the spatial patterns derived from electroencephalogram (EEG) rhythmic components as the feature description. The EEGs are decomposed into a series of frequency bands, and the instantaneous power is represented by the envelop of oscillatory activity, which forms the spatial patterns for a given electrode montage at a time-frequency grid. Time-frequency weights determined by training process are used to synthesize the contributions from the time-frequency domains. The present method was tested in nine human subjects performing left or right hand movement imagery tasks. The overall classification accuracies for nine human subjects were about 80% in the 10-fold cross-validation, without rejecting any trials from the dataset. The loci of MI activity were shown in the spatial topography of differential-mode patterns over the sensorimotor area. The present method does not contain a priori subject-dependent parameters, and is computationally efficient. The testing results are promising considering the fact that no trials are excluded due to noise or artifact. The present method promises to provide a useful alternative as a general purpose classification procedure for MI classification.
AB - To develop a single trial motor imagery (MI) classification strategy for the brain-computer interface (BCI) applications by using time-frequency synthesis approach to accommodate the individual difference, and using the spatial patterns derived from electroencephalogram (EEG) rhythmic components as the feature description. The EEGs are decomposed into a series of frequency bands, and the instantaneous power is represented by the envelop of oscillatory activity, which forms the spatial patterns for a given electrode montage at a time-frequency grid. Time-frequency weights determined by training process are used to synthesize the contributions from the time-frequency domains. The present method was tested in nine human subjects performing left or right hand movement imagery tasks. The overall classification accuracies for nine human subjects were about 80% in the 10-fold cross-validation, without rejecting any trials from the dataset. The loci of MI activity were shown in the spatial topography of differential-mode patterns over the sensorimotor area. The present method does not contain a priori subject-dependent parameters, and is computationally efficient. The testing results are promising considering the fact that no trials are excluded due to noise or artifact. The present method promises to provide a useful alternative as a general purpose classification procedure for MI classification.
KW - Brain-computer interface (BCI)
KW - Electroencephalography (EEG)
KW - Event-related desynchronization (ERD)
KW - Motor imagery
KW - Spatial correlation
KW - Time-frequency weighting
UR - http://www.scopus.com/inward/record.url?scp=8844271035&partnerID=8YFLogxK
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U2 - 10.1016/j.clinph.2004.06.022
DO - 10.1016/j.clinph.2004.06.022
M3 - Article
C2 - 15546783
AN - SCOPUS:8844271035
VL - 115
SP - 2744
EP - 2753
JO - Clinical Neurophysiology
JF - Clinical Neurophysiology
SN - 1388-2457
IS - 12
ER -