MED: Muse™-based Eye-blink Detection Algorithm Using a Single EEG Channel

E. Shachar, A. Lev, O. Rosen
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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.
MED:基于Muse™的使用单个EEG通道的眨眼检测算法
在脑电图(EEG)信号中,眨眼既可以看作是不必要的噪声,也可以看作是信息源。在这两种情况下,都需要可靠而准确的检测器。由于许多应用需要实时检测和处理眨眼,因此检测器需要快速和简单。在这项工作中,我们开发了一种非学习算法,用于从EEG信号中检测和提取眨眼段。这些信号由Muse™记录,Muse™是一种用于娱乐的便携式脑电图设备。该算法通过几个确定性的处理步骤来检测眨眼。该算法通过提取脑电图眨眼信号的独特特征,提取两个主要眨眼阶段脑电图信号中出现的峰值。该算法采用多种预处理步骤来确保检测的鲁棒性,并进行多项安全性检查以防止检测到假峰值和部分眨眼。已经创建了一个数据集,其中每个记录的长度约为20秒,取自几个不同的主题。眨眼注释是手工制作的。该算法在获得的数据集上获得100%的准确率,同时采用了一组确定性操作,使其可用于低资源、实时应用。
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