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BrainEmoNet:基于大脑功能不对称的情绪识别网络

Lizheng Pan¹, Zetong Wang¹, Zhicheng Xu¹

¹School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, People's Republic of China.

Biomedical physics & engineering express

|November 11, 2025

概括

这项研究介绍了BrainEmoNet,这是一个基于电脑电图 (EEG) 的精确情绪识别的新框架. 该模型利用大脑的不对称性来增强情绪状态的识别,为人机交互提供了一个新的工具.

科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
人工智能的人工智能

背景情况:

人与计算机的互动越来越需要理解用户的情绪状态.
基于脑电图 (EEG) 的情绪识别是具有挑战性的,因为信号的复杂性.
现有的方法很难完全捕捉EEG信号的细微差别,以检测情绪.

研究的目的:

提出一个新的基于学习的框架,BrainEmoNet,以提高基于EEG的情感识别准确性.
利用人类大脑功能的不对称性作为增强情绪识别的关键视角.
开发一种能够从EEG信号中提取全面情绪信息的模型.

主要方法:

开发了BrainEmoNet,这是一个整合频域特征网络 (FFN),长期依赖特征网络 (LDFN) 和空间特征分析网络 (SCAN) 的框架.
FFN和LDFN从每个大脑通道中提取频域和长期依赖特征.
SCAN采用道空间注意力机制,专注于高价值道,并分析时空频率特征.

主要成果:

在DEAP数据集上,BrainEmoNet表现出与最先进的模型相比具有竞争力的性能.
在受试者依赖的实验中,获得了高的识别准确度:86.77%的唤起和82.14%的价值.
独立于对象的实验给出了75.53%的唤起和72.83%的价值的准确性.

结论:

拟议的BrainEmoNet通过分析时间-频率-空间特征,有效地改善基于EEG的情绪识别.
BrainEmoNet为理解和监测情绪状态提供了一种有前途的方法.
该模型可以作为一种辅助工具,用于在人机交互场景中评估情绪.

关键词:

这是一个EEGEEGEEGEEGEEGEEGEEG.深度学习是一种深度学习.情感识别情感识别情感识别特性提取特性提取多视角特征模型多视角特征模型

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