{"title":"MED:基于Muse™的使用单个EEG通道的眨眼检测算法","authors":"E. Shachar, A. Lev, O. Rosen","doi":"10.1109/SPMB55497.2022.10014708","DOIUrl":null,"url":null,"abstract":"Eye-blinks in electroencephalogram (EEG) signals can be regarded either as unwanted noise or as a source of information. In both cases, a reliable and accurate detector is needed. As many applications require detection and processing of eye-blinks in real-time, detectors are required to be fast and simple. In this work, we have developed a non-learning algorithm for the detection and extraction of eye-blink segments from EEG signals. The signals were recorded by Muse™, a portable EEG device for recreational use. The proposed algorithm detects eye-blinks via several deterministic processing steps. The algorithm extracts peaks occurring in the EEG signal during the two main eye-blink phases, via extraction of unique features of the EEG eye-blink signal. The proposed algorithm applies various pre-processing steps to ensure robust detection, as well as several sanity-checks to prevent the detection of false peaks and partial eye-blinks. A dataset with recordings of the length of approximately 20 seconds each, taken from few different subjects has been created. The eye-blink annotations were made manually. The proposed algorithm obtains an accuracy rate of 100% on the obtained dataset, while employing a set of deterministic operations which renders it usable in low-resource, real-time applications.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MED: Muse™-based Eye-blink Detection Algorithm Using a Single EEG Channel\",\"authors\":\"E. Shachar, A. Lev, O. Rosen\",\"doi\":\"10.1109/SPMB55497.2022.10014708\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Eye-blinks in electroencephalogram (EEG) signals can be regarded either as unwanted noise or as a source of information. In both cases, a reliable and accurate detector is needed. As many applications require detection and processing of eye-blinks in real-time, detectors are required to be fast and simple. In this work, we have developed a non-learning algorithm for the detection and extraction of eye-blink segments from EEG signals. The signals were recorded by Muse™, a portable EEG device for recreational use. The proposed algorithm detects eye-blinks via several deterministic processing steps. The algorithm extracts peaks occurring in the EEG signal during the two main eye-blink phases, via extraction of unique features of the EEG eye-blink signal. The proposed algorithm applies various pre-processing steps to ensure robust detection, as well as several sanity-checks to prevent the detection of false peaks and partial eye-blinks. A dataset with recordings of the length of approximately 20 seconds each, taken from few different subjects has been created. The eye-blink annotations were made manually. The proposed algorithm obtains an accuracy rate of 100% on the obtained dataset, while employing a set of deterministic operations which renders it usable in low-resource, real-time applications.\",\"PeriodicalId\":261445,\"journal\":{\"name\":\"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPMB55497.2022.10014708\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPMB55497.2022.10014708","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MED: Muse™-based Eye-blink Detection Algorithm Using a Single EEG Channel
Eye-blinks in electroencephalogram (EEG) signals can be regarded either as unwanted noise or as a source of information. In both cases, a reliable and accurate detector is needed. As many applications require detection and processing of eye-blinks in real-time, detectors are required to be fast and simple. In this work, we have developed a non-learning algorithm for the detection and extraction of eye-blink segments from EEG signals. The signals were recorded by Muse™, a portable EEG device for recreational use. The proposed algorithm detects eye-blinks via several deterministic processing steps. The algorithm extracts peaks occurring in the EEG signal during the two main eye-blink phases, via extraction of unique features of the EEG eye-blink signal. The proposed algorithm applies various pre-processing steps to ensure robust detection, as well as several sanity-checks to prevent the detection of false peaks and partial eye-blinks. A dataset with recordings of the length of approximately 20 seconds each, taken from few different subjects has been created. The eye-blink annotations were made manually. The proposed algorithm obtains an accuracy rate of 100% on the obtained dataset, while employing a set of deterministic operations which renders it usable in low-resource, real-time applications.