用LSTM增强的多视图动态情绪图表表示用于EEG信号识别
Guixun Xu1, Wenhui Guo1, Yanjiang Wang1
1College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong Province, People's Republic of China.
Journal of neural engineering
|June 21, 2023
概括
本研究介绍了一种LSTM增强的模型,用于从电脑电图 (EEG) 信号的动态情绪识别. 该模型有效地捕捉了时空特征,在情绪分类中表现优于现有的方法.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 电脑电图 (EEG) 信号包含了丰富的情感识别信息.
- 现有的方法往往难以在EEG数据中完全捕捉复杂的时空动态.
研究的目的:
- 开发一个LSTM增强的多视图动态情感图表表示模型,以改进基于EEG的情感识别.
- 有效地整合来自EEG信号的空间拓和时间信息.
主要方法:
- 提出了一个分为两个分支的模型:一个用于动态多视图图形表示学习,另一个用于使用内存学习时间序列信息.
- 该模型动态学习多个图表表示,并使用长短期记忆 (LSTM) 组件来保留关键的时间信息.
- 两个分支的特征被使用平均融合运算符合并,以增强时空表示.
主要成果:
- 在SEED,SEED-IV和DEAP数据集上进行了广泛的独立于受试者的实验.
- 与最先进的方法相比,拟议的方法在识别EEG情绪信号方面表现优异.
结论:
- 用LSTM增强的多视图动态情感图表表示模型有效地从EEG信号中提取歧视性时空特征.
- 这种方法为准确而强大的情绪识别系统提供了有希望的进步.
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