Koike-Akino Toshiaki, Mahajan Ruhi, Marks Tim K, Watanabe Shinji, Tuzel Oncel, Orlik Philip
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:854-858. doi: 10.1109/EMBC.2016.7590835.
We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First, we show the statistical significance of the P300 component in event-related potential (ERP) data from 14-channel EEGs across 25 subjects. We then apply a variety of machine learning techniques, comparing the user identification performance of various different combinations of a dimensionality reduction technique followed by a classification algorithm. Experimental results show that an identification accuracy of 72% can be achieved using only a single 800 ms ERP epoch. In addition, we demonstrate that the user identification accuracy can be significantly improved to more than 96.7% by joint classification of multiple epochs.
我们分析通过消费级脑电图（EEG）设备获取的脑电波，以研究其用于用户识别和认证的能力。首先，我们展示了来自25名受试者的14通道脑电图中事件相关电位（ERP）数据中P300成分的统计显著性。然后，我们应用各种机器学习技术，比较降维技术与分类算法的各种不同组合的用户识别性能。实验结果表明，仅使用单个800毫秒的ERP时段即可实现72%的识别准确率。此外，我们证明，通过对多个时段进行联合分类，用户识别准确率可显著提高至96.7%以上。
