EEG Classification for Visual Brain Decoding via Metric Learning

Rahul Mishra, Arnav Bhavsar

Abstract

In this work, we propose CNN based approaches for EEG classification which is acquired from a visual perception task involving different classes of images. Our approaches involve deep learning architectures using 1D CNN (on time axis) followed by 1D CNN (on channel axis) and Siamese network (for metric learning) which are novel in this domain. The proposed approaches outperform the state-of-the-art methods on the same dataset. Finally, we also suggest a method to select fewer number of EEG channels.

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Paper Citation


in Harvard Style

Mishra R. and Bhavsar A. (2021). EEG Classification for Visual Brain Decoding via Metric Learning.In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING, ISBN 978-989-758-490-9, pages 160-167. DOI: 10.5220/0010270501600167


in Bibtex Style

@conference{bioimaging21,
author={Rahul Mishra and Arnav Bhavsar},
title={EEG Classification for Visual Brain Decoding via Metric Learning},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING,},
year={2021},
pages={160-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010270501600167},
isbn={978-989-758-490-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING,
TI - EEG Classification for Visual Brain Decoding via Metric Learning
SN - 978-989-758-490-9
AU - Mishra R.
AU - Bhavsar A.
PY - 2021
SP - 160
EP - 167
DO - 10.5220/0010270501600167