Towards the Effective Intrinsic Mode Functions for Motor Imagery EEG Signal Classification

To better utilize one of the most powerful signal decomposition methods called Empirical Mode Decomposition (EMD) in the field of a brain-computer interface, a better understanding of its components is necessary. In analyzing two-class motor imagery Electroencephalogram, one or more specific intrinsic mode function (IMF) plays a major role in the classification and therefore, utilization of the signal. This research work investigates the most effective IMF for motor imagery EEG signal by PCA based Hilber-Huang transformation. To implement the research work, motor imagery (left and right hand) EEG signals of eight participants are taken into consideration. The signals of central lobes are transformed into three dimensions to one dimension by PCA. These transformed signals are decomposed into 8 IMFs. Significant features are extracted from the signals and classified by ANN and LDA method and tabulated. From the results, we found that IMF4 provides the most significant classification accuracies.