Abstract
Brain-computer interfaces are a promising technology for applications ranging from rehabilitation to video-games. A common problem for these systems is the ability to classify correctly signals corresponding to different subjects, as a consequence these systems are trained individually for each person. In this paper several classification methods, along with regularization methods, are compared, to establish a baseline for common datasets in the motor imagery paradigm for intra-subject classification and measure how they influence inter-subject classification.
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Acknowledgement
H. Sossa and E. Zamora would like to acknowledge the support provided by CIC-IPN in carrying out this research. This work was economically supported by SIP-IPN (grant numbers 20200651, 20210316 and 20210788), CONACYT Fronteras de la Ciencia 65 and FORDECYT-PRONACES 60055. CE Solórzano-Espíndola acknowledges CONACYT for the scholarship granted towards pursuing his postgraduate studies.
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Solórzano-Espíndola, C.E., Sossa, H., Zamora, E. (2021). A Comparison Study of EEG Signals Classifiers for Inter-subject Generalization. In: Roman-Rangel, E., Kuri-Morales, Á.F., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2021. Lecture Notes in Computer Science(), vol 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_29
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