Fuzzy entropy for Feature optimization In Motor Imagery based Brain Computer Interface
Abstract
In non-invasive Motor Imagery (MI) based
Brain Computer Interface, variation due to MI has spread not
only in time domain but also in frequency domain. Even
channels are also occupied by this spread. Thus number of
features belonging to all these variations is responsible for
classifying the underlying task. This paper works on feature
optimization using fuzzy entropy so as to avoid under as well
over fitting of classifier. Time-Frequency correlation of the
signal is obtained using wavelet transform. Second and third
order statistical features are extracted from wavelet bands.
SVM and KNN with kernel variations are used for
classification. Outcome of this experimenting leads to accuracy
of 93.7% for optimized features using fuzzy entropy compared
to less than 90% for features without optimization.
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