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Classification of MI EEG Signal Using Minimum Set of Channels to Control a Lower Limb Assistive Device


The research application based on BCI (Brain-Computer Interface) technology is increasing significantly at the present time. The preliminary focus for all BCI related activities is the feature extraction and classification of raw EEG (Electroencephalogram) signals to determine the tasks associated with it. In the beginning, many channels are used to record the signals. Analysis of every channels during performing task makes the classification system very heavy due to the presence of various not so useful information. Therefore, in the present work, only three out of sixty channels from a MI (Motor Imagery)-based EEG database are selected and the effect of reducing channels are discussed. Two feature extraction techniques viz. cross-correlation and wavelet energy have been used. Four MI tasks have been identified using LDA (Linear Discriminate Analysis) classifier from the obtained feature sets. The best average classification accuracy for binary class from three subjects found as 87.50%, 88.89% and 80.95%, respectively. Further, a comparative study with the published work has been performed and it has been observed that the proposed classification methods provide satisfactory results. After successful classification of the EEG signals, one assistive robotic device called lower limb exoskeleton has been triggered with the different MI events. Hence, the present study enhances the recent trends of BCI by implementing the feasibility of a minimum set of channels for MI related BCI application exclusively.

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Roy, G., Bhaumik, S. Classification of MI EEG Signal Using Minimum Set of Channels to Control a Lower Limb Assistive Device. J. Inst. Eng. India Ser. B (2022).

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  • BCI
  • Feature extraction
  • Machine learning
  • Lower limb exoskeleton