{"title":"基于加权共同空间模式的多源脑电图时间序列适应正则化","authors":"","doi":"10.1016/j.compeleceng.2024.109680","DOIUrl":null,"url":null,"abstract":"<div><div>Brain–computer interfaces (BCIs) have garnered significant attention due to their ability to actualize previously fantastical concepts through enabling direct communication between the brain and peripherals. However, electroencephalogram (EEG) time series are inherently vulnerable and subject-specific, necessitating a calibration process that is both intricate and time-consuming for different subjects. To address this issue, we present a feature fusion based adaptation regularization algorithm named as weighted common spatial pattern feature-based adaptation regularization (WCSPAR) to improve the classification performance for multi-source motor imagery EEG signals. Specifically, to leverage information from source domains, we refine the method for constructing covariance matrices within the common spatial pattern framework by incorporating information from source domains and introducing a classifier to predict pseudo labels in target domain. Furthermore, to fully exploit the inter-domain information, we present a similarity estimation approach utilizing Riemannian distance to quantify different contributions from different source domains. Additionally, we devise an uncertainty-free classifier based on adaptation regularization transfer learning to prevent negative transfer. To evaluate the performance of WCSPAR, we conduct comparative experiments involving eight benchmark algorithms. Experimental results demonstrate the effectiveness of WCSPAR, which achieved the highest average accuracy of 80.75% when compared with other state-of-the-art algorithms.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Weighted common spatial pattern based adaptation regularization for multi-source EEG time series\",\"authors\":\"\",\"doi\":\"10.1016/j.compeleceng.2024.109680\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Brain–computer interfaces (BCIs) have garnered significant attention due to their ability to actualize previously fantastical concepts through enabling direct communication between the brain and peripherals. However, electroencephalogram (EEG) time series are inherently vulnerable and subject-specific, necessitating a calibration process that is both intricate and time-consuming for different subjects. To address this issue, we present a feature fusion based adaptation regularization algorithm named as weighted common spatial pattern feature-based adaptation regularization (WCSPAR) to improve the classification performance for multi-source motor imagery EEG signals. Specifically, to leverage information from source domains, we refine the method for constructing covariance matrices within the common spatial pattern framework by incorporating information from source domains and introducing a classifier to predict pseudo labels in target domain. Furthermore, to fully exploit the inter-domain information, we present a similarity estimation approach utilizing Riemannian distance to quantify different contributions from different source domains. Additionally, we devise an uncertainty-free classifier based on adaptation regularization transfer learning to prevent negative transfer. To evaluate the performance of WCSPAR, we conduct comparative experiments involving eight benchmark algorithms. Experimental results demonstrate the effectiveness of WCSPAR, which achieved the highest average accuracy of 80.75% when compared with other state-of-the-art algorithms.</div></div>\",\"PeriodicalId\":50630,\"journal\":{\"name\":\"Computers & Electrical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Electrical Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0045790624006074\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Electrical Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0045790624006074","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
Weighted common spatial pattern based adaptation regularization for multi-source EEG time series
Brain–computer interfaces (BCIs) have garnered significant attention due to their ability to actualize previously fantastical concepts through enabling direct communication between the brain and peripherals. However, electroencephalogram (EEG) time series are inherently vulnerable and subject-specific, necessitating a calibration process that is both intricate and time-consuming for different subjects. To address this issue, we present a feature fusion based adaptation regularization algorithm named as weighted common spatial pattern feature-based adaptation regularization (WCSPAR) to improve the classification performance for multi-source motor imagery EEG signals. Specifically, to leverage information from source domains, we refine the method for constructing covariance matrices within the common spatial pattern framework by incorporating information from source domains and introducing a classifier to predict pseudo labels in target domain. Furthermore, to fully exploit the inter-domain information, we present a similarity estimation approach utilizing Riemannian distance to quantify different contributions from different source domains. Additionally, we devise an uncertainty-free classifier based on adaptation regularization transfer learning to prevent negative transfer. To evaluate the performance of WCSPAR, we conduct comparative experiments involving eight benchmark algorithms. Experimental results demonstrate the effectiveness of WCSPAR, which achieved the highest average accuracy of 80.75% when compared with other state-of-the-art algorithms.
期刊介绍:
The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency.
Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.