Mel Frequency Cepstral Coefficients Based Pattern Recognition for Limb Motor Action
نویسنده
چکیده
This paper proposes a Mel Frequency Cepstral Coefficient (MFCC) based hybrid algorithm for motor imagery classification of Electroencephalogram (EEG) signal for Brain Computer Interface (BCI). The proposed hybrid algorithm contains MFCC with Hjorth Parameter. Regression coefficient method was used for eye artifacts cancellation. The feature extraction method based on the difference of the different hjorth parameters taken from the cepstral coefficients. The extracted features from the cepstral coefficients were classified using two linear classifiers.
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