Affective EEG-Based Person Identification Using the Deep Learning Approach

• Published in: IEEE transactions on cognitive and developmental systems Vol. 12; no. 3; pp. 486 - 496
• Main Authors: Wilaiprasitporn, Theerawit, Ditthapron, Apiwat, Matchaparn, Karis, Tongbuasirilai, Tanaboon, Banluesombatkul, Nannapas, Chuangsuwanich, Ekapol
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affect (Psychology); Affective computing; Artificial neural networks; biometrics; Biometrics (access control); Brain modeling; Cognitive tasks; convolutional neural networks (CNNs); Datasets; Deep learning; deep learning (DL); Electroencephalography; electroencephalography (EEG); Feature extraction; Logic gates; long short-term memory (LSTM); Machine learning; Neural networks; Performance enhancement; personal identification (PI); Power spectral density; Recurrent neural networks; recurrent neural networks (RNNs); Spatial data; State-of-the-art reviews; Support vector machines; Task analysis; personal identification (PI); Biometrics (access control); recurrent neural networks (RNNs); Electroencephalography; Task analysis; deep learning (DL); Deep learning; electroencephalography (EEG); Affective computing; biometrics; convolutional neural networks (CNNs); Logic gates; long short-term memory (LSTM); Feature extraction; Brain modeling

Electroencephalography (EEG) is another method for performing person identification (PI). Due to the nature of the EEG signals, EEG-based PI is typically done while a person is performing a mental task such as motor control. However, few studies used EEG-based PI while the person is in different men...