Effects of wavelets on quality of features in motor-imagery EEG signal classification

This paper examines the quality of feature set obtained from Wavelet based Energy-entropy with variation of scale and wavelet type. Here motor imagery of left-right hand movement classification problem has been studied. Elliptic bandpass filters are used to discard unwanted signals and also to extract alpha & beta rhythms. We have implemented wavelet-based energy-entropy with three level of decomposition in combination with ten wavelet types (Daubechies). We want to identify the best pair of level of decomposition and wavelet type for EEG based motor imagery classifications. We have verified our study with three classifiers- Naïve Bayes, Multi layered Perceptron and Support Vector Machine. The classifiers performance for best wavelet decomposition level is analyzed using evaluation metrics such as accuracy, F-measure and area under ROC.