# Transformer Based Model for Spatiotemporal Feature Learning in EEG Emotion Recognition

Source: [Transformer Based Model for Spatiotemporal Feature Learning in EEG Emotion Recognition](<https://www.sota2.com/research/paper/6a28f37690966ade919c1991>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-11T05:00:14.404Z

Authors: Xinglong Cui, Dian Gu

Published: 2026-06-09T11:25:43.000Z

## 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 feature learning, and 3) a Fuzzy-Attention Synchronous Transformer (FAST) to model spatiotemporal dependencies. Through extensive experiments on three EEG datasets (BETA, SEED, and DepEEG), the proposed model consistently outperforms other methods in terms of classification accuracy and robustness across varying signal lengths. Ablation studies confirm the contribution of the Local Self-Attention Block in improving performance, and the inclusion of depthwise separable convolutions in the decoder reduces computational complexity while maintaining high accuracy. EEG-TransNet's ability to generalize across subjects with minimal performance variation highlights its potential as a robust tool for EEG-based brain activity classification and emotion recognition tasks.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [EEG-based emotion recognition on BETA](<https://www.sota2.com/research/sota/eeg-based-emotion-recognition-on-beta.md>): EEG-based Emotion Recognition; beta; FPS: 120.

- [EEG-based Emotion Recognition on SEED (FPS)](<https://www.sota2.com/research/sota/eeg-based-emotion-recognition-on-seed-fps.md>): EEG-based Emotion Recognition; SEED; FPS: 130.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2606.10718>)

[PDF](<https://arxiv.org/pdf/2606.10718.pdf>)