Electrocorticographic signals classification for brain computer interfaces using stacked-autoencoders

Sandra Cancino; Jaime Delgado Saa

doi:10.1117/12.2568996

20 August 2020 Electrocorticographic signals classification for brain computer interfaces using stacked-autoencoders

Sandra Cancino, Jaime Delgado Saa

Proceedings Volume 11511, Applications of Machine Learning 2020; 115110J (2020) https://doi.org/10.1117/12.2568996
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

Neuro-degenerative diseases can break brain's common output pathways of peripheral nerves and muscles in an individual, inhibiting his ability to perform daily tasks. Brain Computer Interfaces BCI make decoding-encoding of brain signals into control instructions for external devices. This work proposes the use of stacked autoencoders and a softmax layer for classification of visual stimuli from Electrocorticographic (ECoG) signals as an input to the BCI control system. Experimental results show that the proposed method has a good classification performance (average accuracy across subjects 0.95 +/- 0.05), compared to state-of-the-art approaches as Support Vector Machines SVM. Furthermore, the proposed network architecture allows analysis of the weights learned by the classifier making it possible to obtain insights of what signal features the classifier uses to discriminate the visual stimulus.

Conference Presentation

Citation Download Citation

Sandra Cancino and Jaime Delgado Saa "Electrocorticographic signals classification for brain computer interfaces using stacked-autoencoders", Proc. SPIE 11511, Applications of Machine Learning 2020, 115110J (20 August 2020); https://doi.org/10.1117/12.2568996

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