MSLTE: multiple self-supervised learning tasks for enhancing EEG emotion recognition

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL
Guangqiang Li, Ning Chen, Yixiang Niu, Zhangyong Xu, Yuxuan Dong, Jing Jin, Hongqin Zhu
{"title":"MSLTE: multiple self-supervised learning tasks for enhancing EEG emotion recognition","authors":"Guangqiang Li, Ning Chen, Yixiang Niu, Zhangyong Xu, Yuxuan Dong, Jing Jin, Hongqin Zhu","doi":"10.1088/1741-2552/ad3c28","DOIUrl":null,"url":null,"abstract":"<italic toggle=\"yes\">Objective</italic>. The instability of the EEG acquisition devices may lead to information loss in the channels or frequency bands of the collected EEG. This phenomenon may be ignored in available models, which leads to the overfitting and low generalization of the model. <italic toggle=\"yes\">Approach</italic>. Multiple self-supervised learning tasks are introduced in the proposed model to enhance the generalization of EEG emotion recognition and reduce the overfitting problem to some extent. Firstly, channel masking and frequency masking are introduced to simulate the information loss in certain channels and frequency bands resulting from the instability of EEG, and two self-supervised learning-based feature reconstruction tasks combining masked graph autoencoders (GAE) are constructed to enhance the generalization of the shared encoder. Secondly, to take full advantage of the complementary information contained in these two self-supervised learning tasks to ensure the reliability of feature reconstruction, a weight sharing (WS) mechanism is introduced between the two graph decoders. Thirdly, an adaptive weight multi-task loss (AWML) strategy based on homoscedastic uncertainty is adopted to combine the supervised learning loss and the two self-supervised learning losses to enhance the performance further. <italic toggle=\"yes\">Main results</italic>. Experimental results on SEED, SEED-V, and DEAP datasets demonstrate that: (i) Generally, the proposed model achieves higher averaged emotion classification accuracy than various baselines included in both subject-dependent and subject-independent scenarios. (ii) Each key module contributes to the performance enhancement of the proposed model. (iii) It achieves higher training efficiency, and significantly lower model size and computational complexity than the state-of-the-art (SOTA) multi-task-based model. (iv) The performances of the proposed model are less influenced by the key parameters. <italic toggle=\"yes\">Significance</italic>. The introduction of the self-supervised learning task helps to enhance the generalization of the EEG emotion recognition model and eliminate overfitting to some extent, which can be modified to be applied in other EEG-based classification tasks.","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":"48 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of neural engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1741-2552/ad3c28","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Objective. The instability of the EEG acquisition devices may lead to information loss in the channels or frequency bands of the collected EEG. This phenomenon may be ignored in available models, which leads to the overfitting and low generalization of the model. Approach. Multiple self-supervised learning tasks are introduced in the proposed model to enhance the generalization of EEG emotion recognition and reduce the overfitting problem to some extent. Firstly, channel masking and frequency masking are introduced to simulate the information loss in certain channels and frequency bands resulting from the instability of EEG, and two self-supervised learning-based feature reconstruction tasks combining masked graph autoencoders (GAE) are constructed to enhance the generalization of the shared encoder. Secondly, to take full advantage of the complementary information contained in these two self-supervised learning tasks to ensure the reliability of feature reconstruction, a weight sharing (WS) mechanism is introduced between the two graph decoders. Thirdly, an adaptive weight multi-task loss (AWML) strategy based on homoscedastic uncertainty is adopted to combine the supervised learning loss and the two self-supervised learning losses to enhance the performance further. Main results. Experimental results on SEED, SEED-V, and DEAP datasets demonstrate that: (i) Generally, the proposed model achieves higher averaged emotion classification accuracy than various baselines included in both subject-dependent and subject-independent scenarios. (ii) Each key module contributes to the performance enhancement of the proposed model. (iii) It achieves higher training efficiency, and significantly lower model size and computational complexity than the state-of-the-art (SOTA) multi-task-based model. (iv) The performances of the proposed model are less influenced by the key parameters. Significance. The introduction of the self-supervised learning task helps to enhance the generalization of the EEG emotion recognition model and eliminate overfitting to some extent, which can be modified to be applied in other EEG-based classification tasks.
MSLTE:用于增强脑电图情感识别的多重自我监督学习任务
目的。脑电图采集设备的不稳定性可能会导致所采集脑电图的信道或频段信息丢失。现有模型可能会忽略这一现象,从而导致模型过度拟合和泛化程度低。方法为了提高脑电图情绪识别的泛化能力,并在一定程度上减少过拟合问题,我们在所提出的模型中引入了多个自监督学习任务。首先,引入信道掩蔽和频率掩蔽来模拟脑电图不稳定性导致的某些信道和频段的信息丢失,并结合掩蔽图自动编码器(GAE)构建两个基于自监督学习的特征重建任务,以增强共享编码器的泛化能力。其次,为了充分利用这两个自监督学习任务所包含的互补信息,确保特征重建的可靠性,在两个图解码器之间引入了权重共享(WS)机制。第三,采用基于同弹性不确定性的自适应权重多任务损失(AWML)策略,将监督学习损失和两个自监督学习损失结合起来,以进一步提高性能。主要结果在 SEED、SEED-V 和 DEAP 数据集上的实验结果表明(i) 一般来说,在依赖主体和不依赖主体的情况下,所提出的模型比各种基线模型获得了更高的平均情绪分类准确率。(ii) 每个关键模块都有助于提高拟议模型的性能。(iii) 与最先进的基于多任务的模型(SOTA)相比,该模型的训练效率更高,模型大小和计算复杂度明显降低。(iv) 提议模型的性能受关键参数的影响较小。意义重大。自监督学习任务的引入有助于增强脑电情绪识别模型的泛化能力,并在一定程度上消除了过拟合,可将其改进应用于其他基于脑电的分类任务中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信