Comparison of EEG signal decomposition methods in classification of motor-imagery BCI
A brain–computer interface (BCI) provides a link between the human brain and a computer. The task of discriminating four classes (left and right hands and feet) of motor imagery movements of a simple limb-based BCI is still challenging because most imaginary movements in the motor cortex have close spatial representations. We aimed to classify binary limb movements, rather than the direction of movement within one limb. We also investigated joint time-frequency methods to improve classification accuracies. Neither of these, to our knowledge, has been investigated previously in BCI. We recorded EEG data from eleven participants, and demonstrated the classification of four classes of simple-limb motor imagery with an accuracy of 91.46% using intrinsic time-scale decomposition and 88.99% using empirical mode decomposition. In binary classifications, we achieved average accuracies of 89.90% when classifying imaginary movements of left hand versus right hand, 93.1% for left hand versus right foot, 94.00% for left hand versus left foot, 83.82% for left foot versus right foot, 97.62% for right hand versus left foot, and 95.11% for right hand versus right foot. The results show that the binary classification performance is slightly better than that of four-class classification. Our results also show that there is no significant difference in terms of spatial distribution between left and right foot motor imagery movements. There is also no difference in classification performances involving left or right foot movement. This work demonstrates that binary and four-class movements of the left and right feet and hands can be classified using recorded EEG signals of the motor cortex, and an intrinsic time-scale decomposition (ITD) feature extraction method can be used for real time brain computer interface.
KeywordsBrain–computer interface (BCI) Empirical mode decomposition (EMD) Electroencephalography (EEG) Intrinsic time-scale decomposition (ITD) Artificial neural network (ANN)
The authors would like to thank the Universiti Teknologi PETRONAS for the Graduate Assistantship scheme (GA) given to the first author, the Ministry of Education Malaysia for providing Higher Institution Center of Excellence (HICoE) grant (cost center: 0153CA-004) and Centre for Intelligent Signal and Imaging Research (CISIR) for providing facilities and equipment. Also, we wish to thank the participants for their cooperation in the experiments.
EA developed the methodology and collected the data with the guidance of MZY and ASM. EA and IKA performed the analysis and drafted the manuscript. DM and MRB also participated in writing. MZY reviewed and proofread the manuscript. All the authors read and approved the manuscript.
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