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Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets

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Published in:ArXiv cs.LG Recent Papers
Format: Online Article RSS Article
Published: 2026
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spellingShingle Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets
ArXiv cs.LG Recent Papers
Petroleum & Energy
Engineering & Technology
sub_discipline_display Petroleum & Energy
sub_discipline_facet Petroleum & Energy
subject_display ArXiv cs.LG Recent Papers
Petroleum & Energy
Engineering & Technology
ArXiv cs.LG Recent Papers
Petroleum & Energy
Engineering & Technology
subject_facet ArXiv cs.LG Recent Papers
Petroleum & Energy
Engineering & Technology
title Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets
title_auth Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets
title_full Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets
title_fullStr Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets
title_full_unstemmed Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets
title_short Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets
title_sort spatiotemporal convolutions on eeg signal -- a representation learning perspective on efficient and explainable eeg classification with convolutional neural nets
topic ArXiv cs.LG Recent Papers
Petroleum & Energy
Engineering & Technology
url https://arxiv.org/abs/2605.03874v1