Poulos M, Rangoussi M, Alexandris N, Evangelou A
Department of Informatics, University of Piraeus, Greece.
Methods Inf Med. 2002;41(1):64-75.
This paper focusses on the person identification problem based on features extracted from the ElectroEncephaloGram (EEG). A bilinear rather than a purely linear model is fitted on the EEG signal, prompted by the existence of non-linear components in the EEG signal--a conjecture already investigated in previous research works. The novelty of the present work lies in the comparison between the linear and the bilinear results, obtained from real field EEG data, aiming towards identification of healthy subjects rather than classification of pathological cases for diagnosis.
The EEG signal of a, in principle, healthy individual is processed via (non)linear (AR, bilinear) methods and classified by an artificial neural network classifier.
Experiments performed on real field data show that utilization of the bilinear model parameters as features improves correct classification scores at the cost of increased complexity and computations. Results are seen to be statistically significant at the 99.5% level of significance, via the chi 2 test for contingency.
The results obtained in the present study further corroborate existing research, which shows evidence that the EEG carries individual-specific information, and that it can be successfully exploited for purposes of person identification and authentication.
本文聚焦于基于从脑电图（EEG）提取的特征进行人员识别问题。由于EEG信号中存在非线性成分（这一推测已在先前研究工作中探讨过），所以在EEG信号上拟合的是双线性模型而非纯线性模型。本研究的新颖之处在于比较从实际现场EEG数据获得的线性和双线性结果，目的是识别健康受试者而非对病理病例进行诊断分类。
原则上对一名健康个体的EEG信号通过（非）线性（自回归、双线性）方法进行处理，并由人工神经网络分类器进行分类。
对实际现场数据进行的实验表明，将双线性模型参数用作特征可提高正确分类分数，但代价是复杂度和计算量增加。通过卡方列联检验，结果在99.5%的显著性水平上具有统计学意义。
本研究获得的结果进一步证实了现有研究，现有研究表明EEG携带个体特定信息，并且它可成功用于人员识别和认证目的。
