Scholarlink

<p>Spiking neural networks&#xa0;(SNNs), known for their rich spatio-temporal dynamics, have recently gained considerable attention in EEG-based emotion recognition. However, conventional model training approaches often fail to fully exploit the capabilities of SNNs, posing challenges for effective EEG data analysis. In this work, we propose a novel bio-information-fused SNN&#xa0;(BISNN) model to enhance EEG-based emotion recognition. The BISNN model incorporates biologically plausible intrinsic parameters into spiking neurons and is initialized with a structurally equivalent pre-trained ANN model. By constructing a bio-information-fused loss function, the BISNN model enables simultaneous training under dual constraints. Extensive experiments on benchmark EEG-based emotion datasets demonstrate that the BISNN model achieves competitive performance compared to state-of-the-art methods. Additionally, ablation studies investigating various components further elucidate the mechanisms underlying the model’s effectiveness and evolution, aligning well with previous findings.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

BISNN: bio-information-fused spiking neural networks for enhanced EEG-based emotion recognition

  • 期刊论文

  • 发表日期：

    2025年3月22日

查看全文
  • Hongze Sun,
  • Shifeng Mao,
  • Wuque Cai,
  • Yan Cui,
  • Duo Chen,
  • Dezhong Yao,
  • Daqing Guo

摘要

Spiking neural networks (SNNs), known for their rich spatio-temporal dynamics, have recently gained considerable attention in EEG-based emotion recognition. However, conventional model training approaches often fail to fully exploit the capabilities of SNNs, posing challenges for effective EEG data analysis. In this work, we propose a novel bio-information-fused SNN (BISNN) model to enhance EEG-based emotion recognition. The BISNN model incorporates biologically plausible intrinsic parameters into spiking neurons and is initialized with a structurally equivalent pre-trained ANN model. By constructing a bio-information-fused loss function, the BISNN model enables simultaneous training under dual constraints. Extensive experiments on benchmark EEG-based emotion datasets demonstrate that the BISNN model achieves competitive performance compared to state-of-the-art methods. Additionally, ablation studies investigating various components further elucidate the mechanisms underlying the model’s effectiveness and evolution, aligning well with previous findings.