Abstract
A brain-computer interface provides individuals with a way to control a computer. However, most of these interfaces remain mostly utilized in research laboratories due to the absence of certainty and accuracy in the proposed systems. In this work, we acquired our own dataset from seven able-bodied subjects and used Deep Multi-Layer Perceptrons to classify motor imagery encephalography signals into binary (Rest vs Imagined and Left vs Right) and ternary classes (Rest vs Left vs Right). These Deep Multi-Layer Perceptrons were fed with power spectral features computed with the Welch’s averaged modified periodogram method. The proposed architectures outperformed the accuracy achieved by the state-of-the-art for classifying motor imagery bioelectrical brain signals obtaining 88.03%, 85.92% and 79.82%, respectively, and an enhancement of 11.68% on average over the commonly used Support Vector Machines.
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Acknowledgements
E. Zamora, H. Sossa and M. Antelis would like to acknowledge the support provided by UPIITA-IPN, CIC-IPN and Tecnológico de Monterrey, respectively, in carrying out this research. This work was economically supported by SIP-IPN (grant numbers 20180180 and 20180730), and CONACYT grant numbers 65 (Frontiers of Science), 268958 and PN2015-873. F. Arce and G. Hernández acknowledge CONACYT for the scholarship granted towards pursuing their PhD studies.
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Arce, F., Zamora, E., Hernández, G., Antelis, J.M., Sossa, H. (2018). Recognizing Motor Imagery Tasks Using Deep Multi-Layer Perceptrons. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_35
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