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山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (4): 35-39.doi: 10.6040/j.issn.1672-3961.0.2019.679

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基于Bi-LSTM的脑电情绪识别

刘帅1,2,王磊1,2*,丁旭涛1,2   

  1. 1. 河北工业大学省部共建电工装备可靠性与智能化国家重点实验室, 天津 300130;2. 河北工业大学河北省电磁场与电器可靠性重点实验室, 天津 300130
  • 发布日期:2020-08-13
  • 作者简介:刘帅(1993— ),男,山东济南人,硕士研究生,主要研究方向为深度学习,脑认知与神经工程. E-mail:847075008@qq.com. *通信作者简介:王磊(1978— ),男,天津人,副教授,博士,主要研究方向为生物电信号分析,脑机接口. E-mail:murhythm@qq.com
  • 基金资助:
    国家自然科学基金资助项目(31300818);河北省高等学校科学技术研究项目(QN2016097)

Emotional EEG recognition based on Bi-LSTM

LIU Shuai1,2, WANG Lei1,2*, DING Xutao1,2   

  1. 1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China;
    2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China
  • Published:2020-08-13

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摘要： 为解决脑电(electroencephalogram, EEG)情绪识别这一项具有挑战性的任务,提出一种基于双向长短时记忆网络(bidirectional long short-term memory, Bi-LSTM)的脑电情绪分类模型并探索大脑情绪机制,唤醒度准确率最高为76.78%,效价度准确率最高为77.28%,与其他模型比较,Bi-LSTM模型在脑电情绪识别上有出色的表现。通过Bi-LSTM模型对比不同频段、脑区和特征疏密度的准确率来探索大脑情绪机制,表明大脑中情绪相关性最高的频段、脑区和特征疏密度分别为α和β、顶叶区与额叶区、50和15。

关键词: 脑电情绪识别, 深度学习, 情绪, 脑电, 双向长短时记忆网络

Abstract: To solved a challenging task of emotional electroencephalogram(EEG)recognition, this study proposed a bidirectional long short-term memory(Bi-LSTM)EEG classification model and explored the emotional mechanism of the brain, with the highest arousal accuracy of 76.78% and the highest valence accuracy of 77.28%. The Bi-LSTM model, compared with other models, had excellent performances in the recognition of emotional EEG. The Bi-LSTM model was used to explore the brain emotion mechanism by comparing the accuracy of different frequency bands, brain regions and feature density, and the results showed that the frequency bands, brain regions and feature density with the highest emotional correlation in the brain were respectively the α and β regions, Parietal Lobe and Frontal Lobe, 50 and 15.

Key words: EEG emotional recognition, deep learning, emotion, EEG, Bi-LSTM

中图分类号: 

  • TP183
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