Performance of Empirical Mode Decomposition for Frequency Identification in SSVEP Based BCI

Empirical mode decomposition based conventional correlation (EMDCC) method is proposed to identify the frequency components in steady state visual evoked potentials (SSVEP) in electroencephalogram(EEG).The main aim of the proposed EMDCC method is to recognise narrow band frequency components that are present in SSVEP. The study is evaluated on two datasets. The first one is a 40 target benchmark dataset obtained from 35 subjects and the second is a 4 class Inhouse dataset collected from 10 healthy participants. The mean detection accuracy of the conventional correlation method is 85.64 % for the benchmark dataset and it is improved to 93.79 % in the proposed method. The mean detection accuracy of the conventional correlation method is 67.5 % for the Inhouse dataset and it is increased to 82.5 % in the proposed method. The mean detection accuracy of the proposed EMDCC method is also compared to time-weighting canonical correlation analysis (TWCCA) for the benchmark dataset. The mean detection accuracy of TWCCA is 91.04 %. Hence the results show better detection accuracies in the proposed EMDCC method than the simple conventional correlation method and also the existing TWCCA method.