Di Yang, An Xingwei, Zhong Wenxiao, Liu Shuang, Ming Dong
Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.
Front Hum Neurosci. 2021 Sep 13;15:672946. doi: 10.3389/fnhum.2021.672946. eCollection 2021.
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 2 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 13-40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. For inter-experiments classification of REC, the optimized frequency range is 8-40 Hz 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.
在过去几十年里，人们对基于生物信号（如脑电图（EEG）、磁共振成像（MRI））进行身份识别的兴趣与日俱增。先前的研究表明，大脑活动的内在信息可用于在睁眼静息状态（REO）和闭眼静息状态（REC）下识别个体。脑电图（EEG）记录头皮数据，人们认为有噪声的EEG信号会影响实验的准确性，导致结果不可靠。因此，为了进行个体识别，可以研究个体间特征的稳定性和时间鲁棒性。在这项工作中，我们进行了三个时间间隔至少为2周的实验，并使用不同类型的测量方法（功率谱密度、互谱、通道相干性和相位滞后）来提取个体特征。计算皮尔逊相关系数（PCC）以测量个体内部的线性相关程度，并使用支持向量机（SVM）来获得相关的分类准确率。结果表明，对于实验内数据集，四种特征的分类准确率为85% - 100%，对于融合实验数据集，分类准确率为80% - 100%。对于REO特征的实验间分类，功率谱密度、通道相干性和互谱这三个特征的优化频率范围是13 - 40Hz。对于REC的实验间分类，功率谱密度、通道相干性和互谱这三个特征的优化频率范围是8 - 40Hz。相位滞后的分类结果远低于其他三个特征。这些结果表明了EEG的时间鲁棒性，可进一步用于个体识别系统。
