Transformer Based Model for Spatiotemporal Feature Learning in EEG Emotion Recognition

arXiv:2606.10718v1 Announce Type: new Abstract: Electroencephalography (EEG) is a widely adopted technique for monitoring brain activity, offering valuable insights into neurological states due to its high temporal resolution and cost-effectiveness. To enhance the analysis of complex EEG data, we propose EEG-TransNet, an architecture designed to capture temporal, regional, and synchronous features of EEG signals. EEG-TransNet introduces three key modules: 1) a preprocessing and feature extraction module leveraging ResNet and wavelet-based denoising, 2) a Local Self-Attention Block for regional

Source: arXiv cs.LG — read the full report at the original publisher.

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