Abstract Contamination of electroencephalogram (EEG) signals due to natural blinking electrooculogram (EOG) signals is often removed to enhance the quality of EEG signals. This paper discusses the possibility of using solely involuntary blinking signals for human authentication. The EEG data of 46 subjects were recorded while the subject was looking at a sequence of different pictures. During the experiment, the subject was not focused on any kind of blinking task. Having the blink EOG signals separated from EEG, 25 features were extracted and the data were preprocessed in order to handle the corrupt or missing values. Since spontaneous and voluntary blinks have different characteristics in terms of kinematic variables and because the previous studies’ control setup may have altered the type of blink from spontaneous to voluntary, a series of statistical analysis was carried out in order to inspect the changes in the multivariate probability distribution of data compared to the previous studies. Statistical significance shows that it is very likely that the blink features of both voluntary and involuntary blink signal are generated by Gaussian probability density function, although different than voluntary blink, spontaneous blink is not well discriminated with Gaussian. Despite testing several models, none managed to classify the data using only the information of a single spontaneous blink. Thereby, we examined the possibility of learning the patterns of a series of blinks using Gated Recurrent Unit (GRU). Our results show that individuals can be distinguished with up to 98.7% accuracy using only a reasonably short sequence of involuntary blinking signals.

中文翻译：

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自发闪烁在人体认证中的应用

摘要 为了提高EEG信号的质量，通常去除自然闪烁眼电图(EOG)信号对脑电图(EEG)信号的污染。本文讨论了仅使用非自愿眨眼信号进行人类身份验证的可能性。记录了 46 名受试者的 EEG 数据，同时受试者正在查看一系列不同的图片。在实验过程中，受试者没有专注于任何类型的眨眼任务。将眨眼 EOG 信号与 EEG 分离，提取 25 个特征并对数据进行预处理，以处理损坏或缺失的值。由于自发和自愿眨眼在运动学变量方面具有不同的特征，并且由于先前研究的控制设置可能已将眨眼类型从自发改变为自愿，进行了一系列的统计分析，以检查数据的多元概率分布与以前的研究相比的变化。统计显着性表明，自愿和非自愿眨眼信号的眨眼特征很可能是由高斯概率密度函数产生的，尽管与自愿眨眼不同，但自发眨眼与高斯并没有很好的区分。尽管测试了多个模型，但没有一个模型仅使用一次自发眨眼的信息就能够对数据进行分类。因此，我们研究了使用门控循环单元 (GRU) 学习一系列眨眼模式的可能性。我们的结果表明，仅使用相当短的无意识眨眼信号序列，就可以以高达 98.7% 的准确率区分个体。