EEG-based personal identification method using unsupervised feature extraction and its robustness against intra-subject variability
EEG-based personal identification method using unsupervised feature extraction and its robustness against intra-subject variability
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DOI：
10.1088/1741-2552/ab6d89
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发表时间：
2020-04-01
影响因子：
4
通讯作者：
Ishii，Shin
中科院分区：
文献类型：
--
作者：
Nishimoto，Takashi;Higashi，Hiroshi;Ishii，Shin
脑活动信号是用于个人认证的可能生物标志物。然而，由于测量环境因素和受试者相关因素，它们固有地可变;即使对于相同的任务、受试者和实验设置，脑电图（EEG）信号也可能在几天内不同。这种可变性可能导致单个受试者的多次测量中信号的一致性损失，从而降低基于EEG的个人识别的性能。在这项研究中，我们评估了个人EEG特征的变异性的影响，通过使用我们原来的EEG dataset.ApproachWe收集了20个主题在四个回合（上午和下午，每天两天）的EEG信号。在每一轮中，我们在受试者的头皮上重新安装了一个脑电图帽。为了提取在整个会话中不变的个人EEG特征，我们提出了无监督学习方法;普通字典学习和t分布随机邻居嵌入。为了评估个人识别的性能，我们比较了两种不同的实验设置;测试数据记录在同一轮的训练数据（设置SR）和测试数据记录在不同的轮（设置DR）。主要结果SR的性能优于DR，这表明依赖于轮的特征占主导地位。然而，在DR中40%的准确率，这是显着高于机会水平，表明我们提出的方法鲁棒地提取个人特征的变化，在大多数情况下。此外，我们还评估了一个问题的性能，该问题涉及检测未在身份验证系统中注册的个人。在这个问题中，我们得到了一个类似的结果，轮的可变性影响性能。然而，即使在DR中，我们在检测一些未知对象时也获得了良好的性能。显著性我们发现EEG数据的变化实际上影响了用于个人识别的个人特征。然而，即使考虑到EEG数据的可变性，我们发现我们提出的方法适用于个人身份验证场景，即个人识别和未知检测。
ObjectiveBrain activity signals are possible biomarkers for personal authentication. However, they are inherently variable due to measurement-environment factors and subject-dependent factors; electroencephalography (EEG) signals could be different in days even for the same task, subject, and experimental settings. This variability could cause loss of consistency of the signals across multiple measurements of a single subject, and hence decrease the performance of EEG-based personal identification. In this study, we evaluated the influence of the variability on personal EEG features by using our original EEG dataset.ApproachWe collected EEG signals in twenty subjects across four rounds (morning and afternoon daily for two days). At each round, we reinstalled an EEG cap on the subjects' scalps. To extract personal EEG features that were invariant across the sessions, we proposed unsupervised learning methods; common dictionary learning and t-distributed stochastic neighbor embedding. To assess the performance of personal identification, we compared two different experimental settings; test data recorded in the same round as the training data (Setting SR) and test data recorded in different rounds (Setting DR).Main resultsThe performance in SR was better than that in DR, suggesting that features dependent on the rounds were dominant. However, the 40% accuracy rate in DR, which is significantly higher than the chance level, suggests that our proposed method robustly extracted the personal features against the variability, in most cases. Furthermore, we also evaluated the performance of a problem, which involved detecting individuals who were not registered in the authentication system. In this problem, we obtained a similar result that the variability for the rounds influenced the performance. However, we obtained a good performance in the detection of some unknown subjects even in DR.SignificanceWe found the variability in EEG data actually affected the personal features that were used for personal identification. Even considering the variability in EEG data, however, we found our proposed method is applicable in personal authentication scenarios, ie personal identification and unknown detection.