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<p>A multi-scale sparse temporal autoencoder Transformer (3D-CNN-MSTA-Transformer) is proposed for sentiment classification of EEG signals. The overall framework of this model includes raw EEG data input, 3D feature extraction, CNN feature transformation module, improved MSTA Transformer classification, and classification result output. Extracting three-dimensional features to mine information with emotion recognition value from EEG data; The CNN feature transformation module decomposes 3D convolution kernels to reduce computational costs; The MSTA Transformer classification module consists of four parts: data preparation, sparse time block autoencoder, hidden space embedding, and temporal Transformer. It utilizes multi head self-attention mechanism to achieve sentiment classification. The experiment showed that compared with the classic models ResNet-34, ShuffleNet V2, and MobileNet V2 on the SEED, SEED-IV-1, and SEED-IV-2 datasets, the accuracy, recall, specificity, and F1 score of our method were significantly superior on the three datasets, verifying the effectiveness of the proposed algorithm.</p>

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Fusion of EEG feature extraction and CNN-MSTA transformer emotion recognition classification model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月13日

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  • Wenjuan Chu,
  • Hongbo Wen,
  • Huanyu Liu,
  • Xinghua Zhang,
  • Jing Guo

摘要

A multi-scale sparse temporal autoencoder Transformer (3D-CNN-MSTA-Transformer) is proposed for sentiment classification of EEG signals. The overall framework of this model includes raw EEG data input, 3D feature extraction, CNN feature transformation module, improved MSTA Transformer classification, and classification result output. Extracting three-dimensional features to mine information with emotion recognition value from EEG data; The CNN feature transformation module decomposes 3D convolution kernels to reduce computational costs; The MSTA Transformer classification module consists of four parts: data preparation, sparse time block autoencoder, hidden space embedding, and temporal Transformer. It utilizes multi head self-attention mechanism to achieve sentiment classification. The experiment showed that compared with the classic models ResNet-34, ShuffleNet V2, and MobileNet V2 on the SEED, SEED-IV-1, and SEED-IV-2 datasets, the accuracy, recall, specificity, and F1 score of our method were significantly superior on the three datasets, verifying the effectiveness of the proposed algorithm.