{"title":"CET-attention mechanism impact on the classification of EEG signals","authors":"Mouad Riyad, Abdellah Adib","doi":"10.1007/s12243-025-01071-7","DOIUrl":null,"url":null,"abstract":"<div><p>The attention mechanism enables the processing of the data more efficiently by driving the neural networks to focus on the pertinent information. The increase in performance pushed their wide adoption, including for bio-signal. Multiple researchers explored their use of electroencephalography in many scenarios, including motor imagery. Despite the myriad of implementations, their achievement varies from one subject to another since the signals are delicate. In this paper, we extend our previous research (Riyad and Adib 2024) by suggesting a new implementation. The proposal employs the Convolutional Block Attention Module as a backbone with a few modifications adjusted for the nature of the electroencephalography. It uses three levels of attention that are performed on the channel, time, and electrode individually known as Channel Attention Module (CAM), Time Attention Module (TAM), and Electrode Attention Module (EAM). The compartmentalization authorizes the placing of the attention sub-block in diverse configurations, each with a specific order that impacts the extraction of the feature. Also, we suggest studying them with two structures, one with an early spatial filtering that uses the new block once and a late spatial filtering that uses the attention twice. For the experiments, we test on the dataset 2b of the BCI competition IV. The results show that placing the CAM first and feeding its output to the TAM and EAM boost the performance drastically. For optimal results, it is necessary to use the new attention once at the beginning of the network. Also, it permits an even classification of the classes compared with the others.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"80 and networking","pages":"547 - 555"},"PeriodicalIF":2.2000,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Telecommunications","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s12243-025-01071-7","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The attention mechanism enables the processing of the data more efficiently by driving the neural networks to focus on the pertinent information. The increase in performance pushed their wide adoption, including for bio-signal. Multiple researchers explored their use of electroencephalography in many scenarios, including motor imagery. Despite the myriad of implementations, their achievement varies from one subject to another since the signals are delicate. In this paper, we extend our previous research (Riyad and Adib 2024) by suggesting a new implementation. The proposal employs the Convolutional Block Attention Module as a backbone with a few modifications adjusted for the nature of the electroencephalography. It uses three levels of attention that are performed on the channel, time, and electrode individually known as Channel Attention Module (CAM), Time Attention Module (TAM), and Electrode Attention Module (EAM). The compartmentalization authorizes the placing of the attention sub-block in diverse configurations, each with a specific order that impacts the extraction of the feature. Also, we suggest studying them with two structures, one with an early spatial filtering that uses the new block once and a late spatial filtering that uses the attention twice. For the experiments, we test on the dataset 2b of the BCI competition IV. The results show that placing the CAM first and feeding its output to the TAM and EAM boost the performance drastically. For optimal results, it is necessary to use the new attention once at the beginning of the network. Also, it permits an even classification of the classes compared with the others.
期刊介绍:
Annals of Telecommunications is an international journal publishing original peer-reviewed papers in the field of telecommunications. It covers all the essential branches of modern telecommunications, ranging from digital communications to communication networks and the internet, to software, protocols and services, uses and economics. This large spectrum of topics accounts for the rapid convergence through telecommunications of the underlying technologies in computers, communications, content management towards the emergence of the information and knowledge society. As a consequence, the Journal provides a medium for exchanging research results and technological achievements accomplished by the European and international scientific community from academia and industry.