顶刊CAAI发表论文：基于迁移融合策略的用于跨会话脑电信号解码的多频段Transformer

论文“TF‐MEET: A Transferable Fusion Multi‐band Transformer for Cross‐Session EEG Decoding”被CAAI Transactions on Intelligence Technology 接收

时间：2025-05-07

关键词：脑机接口与智能机器人，文章接收

  近日，硕士研究生袁麒龙在CAAI Transactions on Intelligence Technology (JCR Q1) 上发表题为“TF‐MEET: A Transferable Fusion Multi‐band Transformer for Cross‐Session EEG Decoding”的文章，通讯作者为张枢教授。

  Electroencephalography (EEG) is a widely used neuroimaging technique for decoding brain states. Trans former is gaining attention in EEG signal decoding due to its powerful ability to capture global features. However, relying solely on a single feature extracted by the traditional Transformer model to address the domain shift problem caused by the time variability and complexity of EEG signals is challenging. In this paper, we propose a novel Transferable Fusion Multi-band EEG Transformer (TF-MEET) to enhance the performance of cross-session decoding of EEG signals. TF-MEET is mainly divided into three parts: 1) transform the EEG signals into spatial images and band images; 2) design an encoder to obtain spatial features and band features for the two types of images, and comprehensive fusion features are obtained through the weight adaptive fusion module; 3) cross-session EEG signals decoding is achieved by aligning the joint distribution of different domain features and categories through multi-loss domain adversarial training. Experimental results demonstrate: 1) TF-MEET outperforms other advanced transfer learning methods on two public EEG emotion recognition datasets, SEED and SEED_IV, achieving an accuracy of 91.68% on SEED and 76.21% on SEED_IV; 2) TF-MEET proves the effectiveness of the transferable fusion module; 3) TF MEET can identify explainable activation areas in the brain. We demonstrate that TF-MEET can capture comprehensive, transferable, and interpretable features in EEG signals and perform well in cross-session EEG signals decoding, which can promote the development of brain-computer interface system.

图1：The overall architecture of our proposed framework. 图2：The overview of transferable fusion EEG encoder and dual Transformer layer.