PaperNet: Efficient Temporal Convolutions and Channel Residual Attention for EEG Epilepsy Detection

DOI

10.2991/978-94-6239-664-7_79How to use a DOI?

Keywords

Electroencephalography (EEG); brain-computer interface BCI); deep learning; temporal convolution; residual attention; recurrent networks; mental-state classification; PaperNet

Abstract

Electroencephalography (EEG) signals contain rich temporalspectral structure but are difficult to model due to noise, subject variability, and multi-scale dynamics. Lightweight deep learning models have shown promise, yet many either rely solely on local convolutions or require heavy recurrent modules. This paper presents PaperNet, a compact hybrid architecture that combines temporal convolutions, a channel-wise residual attention module, and a lightweight bidirectional recurrent block which is used for short-window classification. Using the publicly available BEED: Bangalore EEG Epilepsy Dataset, we evaluate PaperNet under a clearly defined subject-independent training protocol and compare it against established and widely used lightweight baselines. The model achieves a macro-F1 of 0.96 on the held-out test set with approximately 0.6M parameters, while maintaining balanced performance across all four classes. An ablation study demonstrates the contribution of temporal convolutions, residual attention, and recurrent aggregation. Channel-wise attention weights further offer insights into electrode relevance. Computational profiling shows that PaperNet remains efficient enough for practical deployment on resource-constrained systems through out the whole process. These results indicate that carefully combining temporal filtering, channel reweighting, and recurrent context modeling can yield strong EEG classification performance without excessive computational cost.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Md Shahriar Sajid
AU  - Abhijit Kumar Ghosh
AU  - Fariha Nusrat
PY  - 2026
DA  - 2026/06/08
TI  - PaperNet: Efficient Temporal Convolutions and Channel Residual Attention for EEG Epilepsy Detection
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 1159
EP  - 1174
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_79
DO  - 10.2991/978-94-6239-664-7_79
ID  - Sajid2026
ER  -