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使用空间单维自我注意的情感识别与大脑有效连接网络和光谱功率的融合特征

Tingwei Jiang¹, Hailing Wang¹, Qing Li²

¹School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201200 China.

Cognitive neurodynamics

|November 7, 2025

概括

这项研究引入了一种使用脑电图 (EEG) 大脑网络特征进行情绪识别的新方法. 这种新的方法通过保存空间信息和采用专门的深度学习模型来提高准确性.

科学领域:

神经科学和人工智能人工智能
大脑与计算机的接口

背景情况:

基于脑电图 (EEG) 的情绪识别对于推进人机交互 (HCI) 至关重要.
当前的方法往往将大脑网络特征与单通道特征连接在一起,失去关键的空间信息并限制深度学习的有效性.

研究的目的:

提出一种新的特征融合策略,将二维大脑有效连接 (BEC) 和一维光谱功率特征结合起来,同时保留空间信息.
引入具有空间单维自我注意 (SAD-1D-CNN) 的双通道1D-CNN,用于从融合数据中提取歧视特征.

主要方法:

开发了一个特征融合策略,以整合2D BEC和1D光谱功率特征,保持空间完整性.
实施了一种SAD-1D-CNN模型,以捕捉融合特征中的空间相关性,利用大脑网络拓和光谱信息.
利用SEED和SEED-IV数据集来评估拟议的方法.

主要成果:

在SEED数据集上达到90.61%的准确性,在SEED-IV上达到82.13%的准确性,比最先进的数据集高2.68%.
通过全面的测试,证明了拟议的融合战略和SAD-1D-CNN的优越性.
视觉化证实了注意力模块能够专注于与情绪相关的大脑区域.

结论:

这部小说的功能融合策略和SAD-1D-CNN显著提高了基于EEG的情感识别性能.
该方法有效地保存空间信息并提取区分特征,为HCI和情感计算提供新的见解.
废弃实验验证了拟议网络中每个组件的贡献.

关键词:

1D-CNN 1D-CNN 是一个数字.大脑有效连接 (BEC)情绪识别情绪识别功能融合的特点是:自我注意力自我注意力

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