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Updated: Jun 27, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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半监督的双部分图形构造与积极的EEG样本选择用于情绪识别.

Bowen Pang¹, Yong Peng^2,3, Jian Gao⁴

¹School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.

Medical & biological engineering & computing

|May 3, 2024

概括

这项研究引入了一种使用电脑电图 (EEG) 信号进行交叉主体情绪识别的新方法. 这种方法有效地处理个人差异,通过构建一个半监督的双部分图形与活跃的样本选择.

关键词:

活动样本选择活动样本选择两分位的图形图表.电脑电图 (EEG) 是一个电脑电图.情绪识别情绪识别半监督学习半监督学习

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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) 信号越来越多地用于情绪识别,因为它们与中枢神经系统有直接联系.
脑电图的非静止性和主体间的变性对准确的跨主体情绪识别模型构成重大挑战.

研究的目的:

提出一种新的半监督的双部分图形结构,使用主动EEG样本选择 (SBGASS) 方法来进行强大的跨主体情绪识别.
为了解决基于EEG的情绪识别中跨主体变异性和负样本的局限性.

主要方法:

通过SBGASS的自适应性学习,SBGASS可以通过双部分图来连接被标记和未标记的EEG样本.
在图形构造过程中,使用主动抽样选择来识别和拒绝负样本 (异常值/噪声).
用SEED-IV数据集进行实验验证.

主要成果:

SBGASS积极拒绝负标记样本,改善双部分图形构造和整体模型性能.
对标记EEG样本可转移性的定量分析显示,随着距离类中心点的距离增加,可转移性下降.
该研究通过获得的投影矩阵研究了情感识别中的空间频率模式,并提高了识别精度.

结论:

拟议的SBGASS方法有效地减轻了基于EEG的情绪识别中的跨主体变异性挑战.
积极的样本选择对于通过减少噪音数据的影响来提高双部分图形构建的稳定性至关重要.
这些发现提供了对样本可转移性和空间频率模式的见解,推进了跨主体情绪识别领域.