Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy.
Stichting Epilepsie Instellingen Nederland (SEIN), 2103SW Heemstede, The Netherlands.
Sensors (Basel). 2020 Nov 17;20(22):6565. doi: 10.3390/s20226565.
The electroencephalogram (EEG) has been proven to be a promising technique for personal identification and verification. Recently, the aperiodic component of the power spectrum was shown to outperform other commonly used EEG features. Beyond that, EEG characteristics may capture relevant features related to emotional states. In this work, we aim to understand if the aperiodic component of the power spectrum, as shown for resting-state experimental paradigms, is able to capture EEG-based subject-specific features in a naturalistic stimuli scenario. In order to answer this question, we performed an analysis using two freely available datasets containing EEG recordings from participants during viewing of film clips that aim to trigger different emotional states. Our study confirms that the aperiodic components of the power spectrum, as evaluated in terms of offset and exponent parameters, are able to detect subject-specific features extracted from the scalp EEG. In particular, our results show that the performance of the system was significantly higher for the film clip scenario if compared with resting-state, thus suggesting that under naturalistic stimuli it is even easier to identify a subject. As a consequence, we suggest a paradigm shift, from task-based or resting-state to naturalistic stimuli, when assessing the performance of EEG-based biometric systems.
脑电图（EEG）已被证明是一种很有前途的个人身份识别和验证技术。最近，研究表明功率谱的非周期性成分优于其他常用的 EEG 特征。除此之外，EEG 特征可能会捕捉到与情绪状态相关的特征。在这项工作中，我们旨在了解在自然刺激场景中，静息态实验范式中显示的功率谱的非周期性成分是否能够捕获基于 EEG 的受试者特定特征。为了回答这个问题，我们使用两个免费提供的数据集进行了分析，这些数据集包含了参与者在观看旨在引发不同情绪状态的电影片段时的 EEG 记录。我们的研究证实，从头皮 EEG 中提取的、以偏移和指数参数评估的功率谱的非周期性成分，能够检测到受试者特定的特征。特别是，我们的结果表明，如果与静息状态相比，系统在电影片段场景下的性能显著更高，这表明在自然刺激下，识别主体甚至更加容易。因此，我们建议在评估基于 EEG 的生物识别系统的性能时，从基于任务或静息状态范式转变为自然刺激范式。
