{"title":"ART: 用于重构无噪声多通道脑电信号的去伪变压器","authors":"Chun-Hsiang Chuang, Kong-Yi Chang, Chih-Sheng Huang, Anne-Mei Bessas","doi":"arxiv-2409.07326","DOIUrl":null,"url":null,"abstract":"Artifact removal in electroencephalography (EEG) is a longstanding challenge\nthat significantly impacts neuroscientific analysis and brain-computer\ninterface (BCI) performance. Tackling this problem demands advanced algorithms,\nextensive noisy-clean training data, and thorough evaluation strategies. This\nstudy presents the Artifact Removal Transformer (ART), an innovative EEG\ndenoising model employing transformer architecture to adeptly capture the\ntransient millisecond-scale dynamics characteristic of EEG signals. Our\napproach offers a holistic, end-to-end denoising solution for diverse artifact\ntypes in multichannel EEG data. We enhanced the generation of noisy-clean EEG\ndata pairs using an independent component analysis, thus fortifying the\ntraining scenarios critical for effective supervised learning. We performed\ncomprehensive validations using a wide range of open datasets from various BCI\napplications, employing metrics like mean squared error and signal-to-noise\nratio, as well as sophisticated techniques such as source localization and EEG\ncomponent classification. Our evaluations confirm that ART surpasses other\ndeep-learning-based artifact removal methods, setting a new benchmark in EEG\nsignal processing. This advancement not only boosts the accuracy and\nreliability of artifact removal but also promises to catalyze further\ninnovations in the field, facilitating the study of brain dynamics in\nnaturalistic environments.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":"75 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals\",\"authors\":\"Chun-Hsiang Chuang, Kong-Yi Chang, Chih-Sheng Huang, Anne-Mei Bessas\",\"doi\":\"arxiv-2409.07326\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artifact removal in electroencephalography (EEG) is a longstanding challenge\\nthat significantly impacts neuroscientific analysis and brain-computer\\ninterface (BCI) performance. Tackling this problem demands advanced algorithms,\\nextensive noisy-clean training data, and thorough evaluation strategies. This\\nstudy presents the Artifact Removal Transformer (ART), an innovative EEG\\ndenoising model employing transformer architecture to adeptly capture the\\ntransient millisecond-scale dynamics characteristic of EEG signals. Our\\napproach offers a holistic, end-to-end denoising solution for diverse artifact\\ntypes in multichannel EEG data. We enhanced the generation of noisy-clean EEG\\ndata pairs using an independent component analysis, thus fortifying the\\ntraining scenarios critical for effective supervised learning. We performed\\ncomprehensive validations using a wide range of open datasets from various BCI\\napplications, employing metrics like mean squared error and signal-to-noise\\nratio, as well as sophisticated techniques such as source localization and EEG\\ncomponent classification. Our evaluations confirm that ART surpasses other\\ndeep-learning-based artifact removal methods, setting a new benchmark in EEG\\nsignal processing. This advancement not only boosts the accuracy and\\nreliability of artifact removal but also promises to catalyze further\\ninnovations in the field, facilitating the study of brain dynamics in\\nnaturalistic environments.\",\"PeriodicalId\":501034,\"journal\":{\"name\":\"arXiv - EE - Signal Processing\",\"volume\":\"75 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - EE - Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.07326\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07326","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals
Artifact removal in electroencephalography (EEG) is a longstanding challenge
that significantly impacts neuroscientific analysis and brain-computer
interface (BCI) performance. Tackling this problem demands advanced algorithms,
extensive noisy-clean training data, and thorough evaluation strategies. This
study presents the Artifact Removal Transformer (ART), an innovative EEG
denoising model employing transformer architecture to adeptly capture the
transient millisecond-scale dynamics characteristic of EEG signals. Our
approach offers a holistic, end-to-end denoising solution for diverse artifact
types in multichannel EEG data. We enhanced the generation of noisy-clean EEG
data pairs using an independent component analysis, thus fortifying the
training scenarios critical for effective supervised learning. We performed
comprehensive validations using a wide range of open datasets from various BCI
applications, employing metrics like mean squared error and signal-to-noise
ratio, as well as sophisticated techniques such as source localization and EEG
component classification. Our evaluations confirm that ART surpasses other
deep-learning-based artifact removal methods, setting a new benchmark in EEG
signal processing. This advancement not only boosts the accuracy and
reliability of artifact removal but also promises to catalyze further
innovations in the field, facilitating the study of brain dynamics in
naturalistic environments.