Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics
Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics
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DOI：
10.1016/j.neucom.2015.04.025
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发表时间：
2015-10-20
期刊：
影响因子：
6
通讯作者：
Laszlo, Sarah
中科院分区：
文献类型：
--
作者：
Armstrong, Blair C.;Ruiz-Blondet, Maria V.;Laszlo, Sarah
人类大脑不断地产生代表神经交流的电势。这些电位可以在头皮测量，并构成脑电(EEG)。当脑电被时间锁定到刺激时--比如一个单词的呈现--并在许多这样的呈现上求平均，就得到了事件相关电位(ERP)。很好地理解了ERP的组件的功能特征，并且一些组件表示可能因个体而唯一地不同的处理，例如表示对语义网络的访问的N400组件。我们将几个模式分类器应用于代表个体对不同个体特殊熟悉的文本流的反应的事件相关电位。结果表明，事件相关电位具有可靠的可识别特征，能够可靠地将事件相关电位标记为个体，准确率高于概率(在82-97%的范围内)。此外，这些特征随着时间的推移是稳定的，这表明在长达六个月的滞后之后，从ERPS中继续准确地识别个人。更好的是，在所有情况下，只使用头皮上的3个电极就实现了高度的标记准确性，这是可以获得干净数据的最小可能数量。(C)2015爱思唯尔B.V.保留所有权利。
The human brain continually generates electrical potentials representing neural communication. These potentials can be measured at the scalp, and constitute the electroencephalogram (EEG). When the EEG is time-locked to stimulation - such as the presentation of a word - and averaged over many such presentations, the Event-Related Potential (ERP) is obtained. The functional characteristics of components of the ERP are well understood, and some components represent processing that may differ uniquely from individual to individual such as the N400 component, which represents access to the semantic network. We applied several pattern classifiers to ERPs representing the response of individuals to a stream of text designed to be idiosyncratically familiar to different individuals. Results indicate that there are robustly identifiable features of the ERP that enable labeling of ERPs as belonging to individuals with accuracy reliably above chance (in the range of 82-97%). Further, these features are stable over time, as indicated by continued accurate identification of individuals from ERPs after a lag of up to six months. Even better, the high degree of labeling accuracy achieved in all cases was achieved with the use of only 3 electrodes on the scalp the minimal possible number that can acquire clean data. (C) 2015 Elsevier B.V. All rights reserved.