「Examination and It’s Evaluation of Preprocessing Method for Individual Identification in EEG 」
Examination and It’s Evaluation of Preprocessing Method for Individual Identification in EEG
［Journal of Information Processing Vol.28, pp.239-246］
Recently, the technology of BMI that communicates with humans and operates a robot using human brain information has been actively studied. The authentification function using BMI has been studied by previous research. Although many studies focus on feature extraction and learning model creation, there are few studies that discuss the effectiveness of preprocessing. In this study, we implemented an EEG biometric function using image stimulation method. In this paper, we proposed biometric authentication system system using EEG at time of image stimulus. At the same time, we evaluated the change in authentication accuracy in order to verify the preprocessing (digital filter, artifact countermeasure, epoch) method in the authentication system. As a result, authentication accuracy is improved by performing the proposed preprocessing. In addition, it was shown that convenience and security were improved when using the system.
［Reasons for the award］
Brain Machine Interface (BMI) is a direct communication method between a human brain and an external device to expand capability of human beings. In recent years, non-invasive devices measuring electroencephalogram (EEG) data in real time has been released as consumer products, and machine learning-based EEG data analysis methods have been actively studied. This paper proposes and evaluates a new BMI-based biometric identification system using EEG data of image stimulus presentation. The proposed system using BMI is difficult to be eavesdropped and achieves 98% accuracy of the personal identification. The paper gives insights to readers in this research field, and may have a great impact in practical application. For the above reasons, this paper deserves an Outstanding Paper Award.
Masato Yamashita received his B.E and M.E degree from Kanazawa Institute of Technology in 2018,2020. He is currently engineer at NTTdocomo,Inc. His research interest include Brain Machine Interface and Artificial Intelligence.
Minoru Nakazawa received his M.E and Ph.D. degrees from Kanazawa Institute of Technology, Ishikawa, Japan, in 1993,1999, respectively. In 1993, he joined Fujitsu Laboratory. He became an assistant, professor at Kanazawa Institute of Technology, in 1996, 2011. His research interests include Autonomous Distributed System, Robotics and Artificial Intelligence, He is a member of IPSJ, IEICE, and IEEE.
Yukinobu Nishikawa received his B.E degree from Osaka University, Osaka, Japan, in 1985.In 1985, he joined Hokuriku Nippon Electric Software. He became a professor at Kanazawa Institute of Technology, in 2014. His research interests include Embedded System, Software development process and Software quality management.
Noriyuki Abe is an associate professor of the Department of Information and Computer Science at Kanazawa Institute of Technology. He received his Master Degree from Kanazawa Institute of Technology in 1986. He then joined Hitachi. He received his Ph.D. in engineering from Kanazawa Institute of Technology in 1991. He became an assistant professor at Kanazawa Institute of Technology in 1991. Currently, his research interests are autonomous distributed systems and SNS data analysis. He is a member of IPSJ, IEICE, and DBSJ.