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基于自适应窗口选择和CA-KANAN的情绪识别框架

Xuefen Lin¹, Linhui Fan¹, Yifan Gu²

¹College of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023 China.

Cognitive neurodynamics

|June 27, 2025

概括

这项研究引入了基于脑电图 (EEG) 的情绪识别的新框架,实现了高精度. 该方法将自适应窗口选择与增强的神经网络相结合,以实现高效可靠的情绪检测.

科学领域:

神经科学是一个神经科学.
人工智能的人工智能
生物医学工程生物医学工程

背景情况:

使用脑电图 (EEG) 数据进行情绪识别对于各种应用至关重要.
改进EEG数据处理和情绪识别模型是一个活跃的研究领域.

研究的目的:

开发基于EEG的情绪识别的先进框架.
为了提高情绪识别模型的准确性和效率.

主要方法:

使用适应性窗口选择的CUSUM算法提取与情绪相关的EEG段.
采用卷积注意力增强的科尔摩戈罗夫-阿诺德网络 (CA-KAN) 进行强大的情绪分类.

主要成果:

在SEED数据集上达到94.63%的峰值分类准确度,在SEED-IV数据集上达到94.73%.
展示了一个适合现实世界部署的轻量级框架.

结论:

拟议的CA-KAN框架显著提高了基于EEG的情绪识别准确性和效率.
该框架显示了医疗情绪监测和驾驶员情绪检测等应用的潜力.

关键词:

适应式窗口选择选择这是一个EEGEEGEEGEEGEEGEEGEEG.情绪识别情绪识别科尔摩戈罗夫-阿诺尔德网络是

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