文献类型：期刊文献

中文题名：融合频率和通道卷积注意的脑电(EEG)情感识别

英文题名：EEG Emotion Recognition Based on Frequency and Channel Convolutional Attention

作者：柴冰[1,2];李冬冬[1,2];王喆[1];高大启[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237;[2]苏州大学计算机信息处理技术重点实验室,江苏苏州215006

年份：2021

卷号：48

期号：12

起止页码：312

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

基金：国家自然科学基金(61806078,62076094,61976091);上海市教育发展基金会和上海市教育委员会“曙光计划”(61725301);国家重大新药开发科技专项(2019ZX09201004);上海市科技计划项目(20511100600)。

语种：中文

中文关键词：脑电情感识别;特征表示;残差网络;预激活残差单元;频率和电极通道卷积注意

外文关键词：EEG emotion recognition;Feature representation;Residual network;Pre-activated residual unit;Frequency and electrode channel convolutional attention

摘要：现有的脑电(EEG)情感识别研究普遍采用神经网络和单一注意机制来学习情感特征,具有相对单一的特征表示。而神经科学研究表明,不同频率和电极通道的脑电信号对情感有不同的响应程度,因此文中提出了一种融合频率和电极通道卷积注意的方法,用于脑电情感识别。具体来说,首先将EEG信号分解到不同的频带上并提取相应的帧级特征,然后用预激活残差网络来学习深层次的脑电情感相关特征,同时在残差网络的每个预激活残差单元中都融入频率和电极通道卷积注意模块,以建模脑电信号的频率和电极通道信息,并生成脑电特征的最终注意表示。在DEAP和DREAMER数据集上的独立于受试者场景下的实验结果表明,所提出的卷积注意方法相比单一注意机制更有助于增强EEG信号中情感显著信息的导入,并且能产生更好的情感识别结果。
The existing emotion recognition researches generally use neural network and attention mechanism to learn emotional features,which have relatively single feature representation.Moreover,neuroscience studies have shown that EEG signals of different frequencies and channels have different responses to emotion.Therefore,this paper proposes a method of fusing frequency and electrode channel convolutional attention for EEG emotion recognition.Specifically,EEG signals are firstly decomposed into different frequency bands and the corresponding frame-level features are extracted.Then the pre-activated residual network is employed to learn deep emotion-relevant features.At the same time,the frequency and electrode channel convolutional attention module is integrated into each pre-activated residual unit of residual network to model the frequency and channel information of EEG signals,thus generating final representation of EEG features.Experiments on DEAP and DREAMER datasets show that the proposed method helps to enhance the importing of emotion-salient information in EEG signals when compared with single-layer attention mechanism,and generates better recognition performance.