Extraction subject-specific motor imagery time–frequency patterns for single trial EEG classification

Abstract We introduce a new adaptive time–frequency plane feature extraction strategy for the segmentation and classification of electroencephalogram (EEG) corresponding to left and right hand motor imagery of a brain–computer interface task. The proposed algorithm adaptively segments the time axis...

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Bibliographic Details
Published in:Computers in biology and medicine 2007-04, Vol.37 (4), p.499-508
Main Authors: Ince, Nuri F, Tewfik, Ahmed H, Arica, Sami
Format: Article
Language:English
Subjects:
Algorithms
Brain–computer interface
Cortical Synchronization - classification
Cosine packets
Dominance, Cerebral - physiology
Electroencephalography - classification
Evoked Potentials - physiology
Fourier Analysis
Functional Laterality - physiology
Humans
Imagination - physiology
Internal Medicine
Linear Models
Local discriminant bases
Motor Cortex - physiology
Motor imagery
Other
Psychomotor Performance - physiology
Signal Processing, Computer-Assisted
Software
Time Perception - physiology
Time–frequency analysis
User-Computer Interface
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Summary:Abstract We introduce a new adaptive time–frequency plane feature extraction strategy for the segmentation and classification of electroencephalogram (EEG) corresponding to left and right hand motor imagery of a brain–computer interface task. The proposed algorithm adaptively segments the time axis by dividing the EEG data into non-uniform time segments over a dyadic tree. This is followed by grouping the expansion coefficients in the frequency axis in each segment. The most discriminative features are selected from the segmented time–frequency plane and fed to a linear discriminant for classification. The proposed algorithm achieved an average classification accuracy of 84.3% on six subjects by selecting the most discriminant subspaces for each one. For comparison, classification results based on an autoregressive model are also presented where the mean accuracy of the same subjects turned out to be 79.5%. Interestingly the subjects and two hemispheres of each subject are represented by distinct segmentations and features. This indicates that the proposed method can handle inter-subject variability when constructing brain–computer interfaces.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2006.08.014