An ongoing interest towards identification based on biosignals, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), is growing in the past decades. Previous studies indicated that the inherent information about brain activity may be used to identify individual during resting-state of eyes open (REO) and eyes closed (REC). Electroencephalographic (EEG) records the data from the scalp, and it is believed that the noisy EEG signals can influence the accuracies of one experiment causing unreliable results. Therefore, the stability and time-robustness of inter-individual features can be investigated for the purpose of individual identification. In this work, we conducted three experiments with the time interval of at least two weeks, and used different types of measures (Power Spectral Density, Cross Spectrum, Channel Coherence and Phase Lags) to extract the individual features. The Pearson Correlation Coefficient (PCC) is calculated to measure the level of linear correlation for intra-individual, and Support Vector Machine (SVM) is used to obtain the related classification accuracy. Results show that the classification accuracies of four features were 85%-100% for intra-experiment dataset, and were 80%-100% for fusion experiments dataset. For inter-experiments classification of REO features, the optimized frequency range is 13-40Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. For inter-experiments classification of REC, the optimized frequency range is 8-40Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. The classification results of Phase Lags are much lower than the other three features. These results show the time-robustness of EEG, which can further use for individual identification system.

中文翻译：

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基于静息态脑电图的个体识别时间鲁棒性分析

在过去的几十年中，人们对基于生物信号（例如脑电图 (EEG)、磁共振成像 (MRI)）的识别产生了持续的兴趣。先前的研究表明，关于大脑活动的固有信息可用于在睁眼 (REO) 和闭眼 (REC) 静息状态下识别个体。脑电图 (EEG) 记录来自头皮的数据，据信嘈杂的 EEG 信号会影响一项实验的准确性，从而导致结果不可靠。因此，可以出于个体识别的目的研究个体间特征的稳定性和时间稳健性。在这项工作中，我们以至少两周的时间间隔进行了三个实验，并使用了不同类型的度量（功率谱密度、交叉谱、通道相干和相位滞后）来提取单个特征。计算 Pearson 相关系数 (PCC) 来衡量个体内部线性相关的程度，并使用支持向量机 (SVM) 获得相关分类准确度。结果表明，实验内数据集的四个特征的分类精度为85%-100%，融合实验数据集为80%-100%。对于 REO 特征的实验间分类，对于功率谱密度、信道相干和交叉谱这三个特征，优化的频率范围为 13-40Hz。对于 REC 的实验间分类，优化的频率范围为 8-40Hz，用于功率谱密度、信道相干和交叉谱三个特征。Phase Lags 的分类结果远低于其他三个特征。这些结果显示了 EEG 的时间鲁棒性，可进一步用于个体识别系统。