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基于正面EEG相关性的人类情绪的识别和分类

S V Thiruselvam¹, M Ramasubba Reddy²

¹Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, India. am18d029@smail.iitm.ac.in.

Physical and engineering sciences in medicine

|November 14, 2024

概括

这项研究开发了一种方法来识别电脑电图 (EEG) 信号中的情绪内容,提高机器对人类情绪的理解. 选择特定的EEG段可以提高情绪分类的准确性,从而改善人机交互.

科学领域:

神经科学和人工智能人工智能
人与计算机的交互
情感计算是一种情感计算.

背景情况:

机器越来越多地参与人类沟通,需要情绪识别能力.
通过电脑电图 (EEG) 等生理信号理解人类情绪对于智能机器辅助至关重要.
准确的情感识别可以增强人机交互,并有助于心理健康监测.

研究的目的:

创建一个特定于情绪的EEG数据集.
开发一种算法来识别EEG信号中的引起情绪的部分.
使用开发的算法对EEG信号进行情绪分类,并将其性能与传统方法进行比较.

主要方法:

脑电图信号被细分为3秒间隔.
通过额头电极相关性下降来确定引起情绪的部分,并通过面部表情来验证.
EEGNet被用于对选定的和所有EEG段的情绪分类.

主要成果:

使用精选的情绪EEG段,情绪分类的准确性比使用所有段更高.
根据具体学科的分类实现了平均准确度为80.87%,分段选择与70.5%没有.
独立于主体的分类在选择细分时获得了67%的准确性,而没有分类时的准确性为63.8%.

结论:

建议的选择情绪引起的EEG段的方法显著提高了情绪分类的准确性.
这种方法提高了开发更具情感智能的机器和有效的心理健康监测工具的潜力.
对DEAP数据集的验证证实了分段选择方法对主体依赖和独立分类的有效性.

关键词:

情绪的分类情绪的分类情感内容部分情感内容部分情绪识别情绪识别额头的EEG电力电流.

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