Person-identification using familiar-name auditory evoked potentials from frontal EEG electrodes. (July 2021)

• Record Type: Journal Article
• Authors: Jijomon, C.M.; Vinod, A.P.
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Auditory evoked potential -- Biometrics -- Deep learning -- Electroencephalogram -- Familiar-name -- Person identification; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102739
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: EEG based person identification is implemented using auditory evoked potentials recorded from two frontal EEG electrodes. Auditory evoked potentials are recorded while subjects listen to the familiar-names including their own-name. The features are extracted using the combination of convolutional and recurrent neural networks. Abstract: Electroencephalograph (EEG) based biometric identification has recently gained increased attention of researchers. However, state-of-the-art EEG-based biometric identification techniques use large number of EEG electrodes, which poses user inconvenience and consumes longer preparation time for practical applications. This work proposes a novel EEG-based biometric identification technique using auditory evoked potentials (AEPs) acquired from two EEG electrodes. The proposed method employs single-trial familiar-name AEPs extracted from the frontal electrodes Fp1 and F7, which facilitates faster and user-convenient data acquisition. The EEG signals recorded from twenty healthy individuals during four experiment trials are used in this study. Different combinations of well-known neural network architectures are used for feature extraction and classification. The cascaded combinations of 1D-convolutional neural networks (1D-CNN) with long short-term memory (LSTM) and with gated recurrent unit (GRU) networks gave the person identification accuracies above 99 %. 1D-convolutional, LSTM network achieves the highest person identification accuracy of 99.53 % and a half total error rate (HTER) of 0.24 % using AEP signals from the two frontal electrodes. With the AEP signals from the single electrode Fp1, the same network achieves a person identification accuracy of 96.93 %. The use of familiar-name AEPs from frontal EEG electrodes that facilitates user convenient data acquisition with shorter preparation time is the novelty of this work.