Muhammad Arif, Faizan Ur Rehman, Lukas Sekanina, Aamir Saeed Malik
{"title":"基于脑电图的应用中的进化算法和元启发式算法综合调查。","authors":"Muhammad Arif, Faizan Ur Rehman, Lukas Sekanina, Aamir Saeed Malik","doi":"10.1088/1741-2552/ad7f8e","DOIUrl":null,"url":null,"abstract":"<p><p>Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG-based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications.\",\"authors\":\"Muhammad Arif, Faizan Ur Rehman, Lukas Sekanina, Aamir Saeed Malik\",\"doi\":\"10.1088/1741-2552/ad7f8e\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG-based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.</p>\",\"PeriodicalId\":94096,\"journal\":{\"name\":\"Journal of neural engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of neural engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1088/1741-2552/ad7f8e\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of neural engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/1741-2552/ad7f8e","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
脑电图(EEG)已成为了解人类大脑复杂运作的主要非侵入性移动模式,为认知过程、神经疾病和脑机接口(BCI)提供了宝贵的见解。然而,脑电图数据量大、存在伪影、需要选择最佳通道以及需要从脑电图数据中提取特征,这些都给用于处理脑电图数据的机器学习算法带来了巨大挑战,使其难以获得有意义和有区别的结果。因此,为了有效克服这些障碍,对复杂优化技术的需求已变得势在必行。近年来,进化算法(EAs)和其他受自然启发的元启发式算法已被用作强大的设计和优化工具,在解决与基于脑 EEG 的应用相关的各种设计和优化问题方面展示了其重要意义。本文全面介绍了 EA 和其他元启发式算法在基于脑电图的应用中的重要性。调查按照 EAs 已应用的主要领域进行组织,即伪影缓解、通道选择、特征提取、特征选择和信号分类。最后,讨论了基于脑电图的应用中 EAs 目前面临的挑战和未来的发展方向。
A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications.
Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG-based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.