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arXiv Computer Vision By Yip Tin Po, Jianming Wang, Yutao Miao, Jiayan Zhang, Yunxu Zhao, Xiaomin Ouyang, Zhihong Li, Nevin L. Zhang

Bidirectional Temporal Dynamics Modeling for EEG-based Driving Fatigue Recognition

Read the original on arXiv Computer Vision →

The paper introduces DeltaGateNet, a framework that models bidirectional temporal dynamics in EEG signals for driving fatigue recognition. It uses a Bidirectional Delta module to separate positive and negative temporal differences, and a Gated Temporal Convolution module to capture long‑term dependencies while preserving channel specificity. Experiments on SEED‑VIG and SADT datasets show that DeltaGateNet outperforms existing methods, achieving high intra‑subject and inter‑subject accuracies across balanced and unbalanced data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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