Considerable interest has been recently devoted to the exploitation of brain activity as biometric identifier in automatic recognition systems, with a major focus on data acquired through electroencephalography (EEG). Several researches have in fact confirmed the presence of discriminative characteristics within brain signals recorded while performing specific mental tasks. Yet, to make EEG-based recognition appealing for practical applications, it would be highly advisable to investigate the existence and permanence of such distinctive traits while performing different mental tasks. In this regard, the present study evaluates the feasibility of performing task-independent EEG-based biometric recognition. A deep learning approach using siamese convolutional neural networks is employed to extract, from the considered EEG recordings, subject-specific template representations. An extensive set of experimental tests, performed on a multi-session database comprising EEG data acquired from 45 subjects while performing six different tasks, is employed to evaluate whether it is actually possible to verify the identity of a subject using brain signals, regardless the performed mental task.

近来，人们对自动识别系统中的脑活动作为生物识别标识符的开发投入了极大的兴趣，主要关注通过脑电图（EEG）获取的数据。实际上，一些研究已经证实，在执行特定的心理任务时，所记录的大脑信号中存在辨别特征。然而，要使基于EEG的识别在实际应用中具有吸引力，强烈建议在执行不同的心理任务时研究这些独特特征的存在和持久性。在这方面，本研究评估了执行基于任务的基于脑电图的生物识别的可行性。采用暹罗卷积神经网络的深度学习方法从考虑的EEG记录中提取，特定于主题的模板表示形式。在多会话数据库上执行的一系列广泛的实验测试包括从45个受试者获取的EEG数据，同时执行六个不同的任务，用于评估实际上是否可以使用脑信号来验证受试者的身份，无论执行了什么精神任务。