EEG artifact removal—state-of-the-art and guidelines

最大熵 工件(错误) 计算机科学 脑电图 独立成分分析 鉴定(生物学) 人工智能 信号(编程语言) 组分(热力学) 最大化 盲信号分离 模式识别(心理学) 干扰(通信) 语音识别 机器学习 数学 精神科 心理学 物理 数学优化 频道(广播) 热力学 生物 植物 程序设计语言 计算机网络
作者
José Antonio Urigüen,Begonya García-Zapirain
出处
期刊:Journal of Neural Engineering [IOP Publishing]
卷期号:12 (3): 031001-031001 被引量:742
标识
DOI:10.1088/1741-2560/12/3/031001
摘要

This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis—to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
酷波er应助科研通管家采纳,获得10
刚刚
gulllluuuukk发布了新的文献求助10
刚刚
斯文败类应助科研通管家采纳,获得10
刚刚
充电宝应助科研通管家采纳,获得20
刚刚
1秒前
2秒前
温暖静柏发布了新的文献求助10
3秒前
哈哈发布了新的文献求助10
3秒前
打打应助hanghang采纳,获得20
3秒前
香蕉觅云应助Qwe采纳,获得10
4秒前
清仔完成签到,获得积分10
4秒前
眼睛大的怀曼完成签到,获得积分10
4秒前
害羞的裘完成签到 ,获得积分10
5秒前
Lyn发布了新的文献求助10
6秒前
7秒前
123发布了新的文献求助10
7秒前
大鱼应助小鼠星球采纳,获得10
8秒前
8秒前
秀丽莛完成签到,获得积分10
10秒前
佳佳应助cis2014采纳,获得10
10秒前
念所三旬完成签到,获得积分10
11秒前
温暖静柏完成签到,获得积分10
12秒前
12秒前
14秒前
温乘云完成签到,获得积分10
14秒前
15秒前
lukawa完成签到,获得积分10
15秒前
15秒前
16秒前
慕青应助DueR采纳,获得10
17秒前
风清扬应助cab_rose采纳,获得10
17秒前
18秒前
18秒前
chase发布了新的文献求助10
19秒前
19秒前
20秒前
佐zzz完成签到 ,获得积分10
21秒前
111222发布了新的文献求助10
21秒前
21秒前
研友_VZG7GZ应助gogoyoco采纳,获得10
22秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989589
求助须知:如何正确求助?哪些是违规求助? 3531795
关于积分的说明 11254881
捐赠科研通 3270329
什么是DOI,文献DOI怎么找? 1804966
邀请新用户注册赠送积分活动 882136
科研通“疑难数据库(出版商)”最低求助积分说明 809176