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一个创新的脑启发的神经竞争模型用于脑电图情感识别

Jiawen Li^1,2,3, Guanyuan Feng¹, Weibin Lin¹

¹School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China.

|November 6, 2025

概括

这项研究引入了一个以大脑为灵感的模型,用于使用电脑电图 (EEG) 信号识别情绪. 该模型实现了高精度,为数字健康应用提供了生物学上可信的方法.

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
数字健康数字健康

背景情况:

情绪识别对于人机交互和临床神经心理学至关重要.
目前用于情绪识别的深度学习模型缺乏可解释性,阻碍了对大脑活动的洞察力.

研究的目的:

提出一种创新的脑启发的神经竞争模型,用于从电脑电图 (EEG) 中识别情绪.
通过模拟生物神经过程来提高情绪识别模型的解释性.

主要方法:

使用离散波量变换的特征提取以获得五个大脑节奏 (三角形,三角形,α,β,gamma).
一个模拟刺激和抑制动态的神经元竞争模型.
节奏注意编码器和泄漏的神经元决策的整合和发射模型.
情绪识别基于一个赢家夺取一切原则.

主要成果:

在DEAP数据集中,平均准确度达到86.64-92.70%.
在SEED数据集上获得了81.89%的准确性.
在DREAMER数据集上达到80.38-82.70%的准确性.
证明不同的大脑节奏在情绪识别任务中具有不同的特征.

结论:

提出了基于EEG的情绪识别的生物可信范式.
为了解大脑活动的神经生理学基础提供了基础.
旨在为医疗保健应用开发可靠的工具,如心理健康监测和抑郁症检测.

关键词:

电脑电图 (电脑电图) 是一种脑电图.大脑节奏大脑节奏大脑启发的模型情感识别情感识别情感识别神经元之间的竞争.

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