FeelNet: A Lightweight Fast Fourier Transform EEG-based Emotion Recognition Network

Xueyao Wang, Xiuding Cai, Yaoyao Zhu, Yu Yao
Proceedings of the 17th Asian Conference on Machine Learning, PMLR 304:479-494, 2025.

Abstract

Emotion recognition using Electroencephalography (EEG) is challenging due to its low signal-to-noise ratios and high-dimensional sparsity. We propose FeelNet, a novel Fast Fourier Transform (FFT)-based architecture that simultaneously extracts global and local features across joint frequency-time domains. FeelNet incorporates an adaptive Rhythm Spectral Block (RSB) for capturing key frequency patterns and filtering task-irrelevant noise through power spectral thresholding. Additionally, the Multi-scale Temporal Conv Block (MTCB) enhances the model’s ability to decode complex temporal dynamics. Extensive evaluations on the DEAP and DREAMER datasets demonstrate that FeelNet outperforms existing state-of-the-art methods in accuracy and flexibility, even under noise-contaminated conditions. Owing to its computational efficiency and noise resilience, FeelNet provides an alternative perspective for EEG-based affective computing.

Cite this Paper

BibTeX
@InProceedings{pmlr-v304-wang25b, title = {FeelNet: A Lightweight Fast Fourier Transform EEG-based Emotion Recognition Network}, author = {Wang, Xueyao and Cai, Xiuding and Zhu, Yaoyao and Yao, Yu}, booktitle = {Proceedings of the 17th Asian Conference on Machine Learning}, pages = {479--494}, year = {2025}, editor = {Lee, Hung-yi and Liu, Tongliang}, volume = {304}, series = {Proceedings of Machine Learning Research}, month = {09--12 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v304/main/assets/wang25b/wang25b.pdf}, url = {https://proceedings.mlr.press/v304/wang25b.html}, abstract = {Emotion recognition using Electroencephalography (EEG) is challenging due to its low signal-to-noise ratios and high-dimensional sparsity. We propose FeelNet, a novel Fast Fourier Transform (FFT)-based architecture that simultaneously extracts global and local features across joint frequency-time domains. FeelNet incorporates an adaptive Rhythm Spectral Block (RSB) for capturing key frequency patterns and filtering task-irrelevant noise through power spectral thresholding. Additionally, the Multi-scale Temporal Conv Block (MTCB) enhances the model’s ability to decode complex temporal dynamics. Extensive evaluations on the DEAP and DREAMER datasets demonstrate that FeelNet outperforms existing state-of-the-art methods in accuracy and flexibility, even under noise-contaminated conditions. Owing to its computational efficiency and noise resilience, FeelNet provides an alternative perspective for EEG-based affective computing.} }
Endnote
%0 Conference Paper %T FeelNet: A Lightweight Fast Fourier Transform EEG-based Emotion Recognition Network %A Xueyao Wang %A Xiuding Cai %A Yaoyao Zhu %A Yu Yao %B Proceedings of the 17th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Hung-yi Lee %E Tongliang Liu %F pmlr-v304-wang25b %I PMLR %P 479--494 %U https://proceedings.mlr.press/v304/wang25b.html %V 304 %X Emotion recognition using Electroencephalography (EEG) is challenging due to its low signal-to-noise ratios and high-dimensional sparsity. We propose FeelNet, a novel Fast Fourier Transform (FFT)-based architecture that simultaneously extracts global and local features across joint frequency-time domains. FeelNet incorporates an adaptive Rhythm Spectral Block (RSB) for capturing key frequency patterns and filtering task-irrelevant noise through power spectral thresholding. Additionally, the Multi-scale Temporal Conv Block (MTCB) enhances the model’s ability to decode complex temporal dynamics. Extensive evaluations on the DEAP and DREAMER datasets demonstrate that FeelNet outperforms existing state-of-the-art methods in accuracy and flexibility, even under noise-contaminated conditions. Owing to its computational efficiency and noise resilience, FeelNet provides an alternative perspective for EEG-based affective computing.
APA
Wang, X., Cai, X., Zhu, Y. & Yao, Y.. (2025). FeelNet: A Lightweight Fast Fourier Transform EEG-based Emotion Recognition Network. Proceedings of the 17th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 304:479-494 Available from https://proceedings.mlr.press/v304/wang25b.html.

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