Affective EEG-Based Person Identification Using Channel Attention Convolutional Neural Dense Connection Network

In the biometric recognition mode, the use of electroencephalogram (EEG) for biometric recognition has many advantages such as anticounterfeiting and nonsteal ability. Compared with traditional biometrics, EEG biometric recognition is safer and more concealed. Generally, EEG-based biometric recognit...

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Bibliographic Details
Published in:Security and communication networks 2021, Vol.2021, p.1-10
Main Authors: Zhang, Yunxia, Li, Xin, Zhao, Changming, Zheng, Wenyin, Wang, Manqing, Zhang, Yongqing, Ma, Hongjiang, Gao, Dongrui
Format: Article
Language:English
Subjects:
Algorithms
Biometric recognition systems
Biometrics
Brain research
Datasets
Deep learning
Electroencephalography
Emotions
Feature extraction
Identification
Multilayer perceptrons
Neural networks
Visual signals
Visual tasks
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Summary:In the biometric recognition mode, the use of electroencephalogram (EEG) for biometric recognition has many advantages such as anticounterfeiting and nonsteal ability. Compared with traditional biometrics, EEG biometric recognition is safer and more concealed. Generally, EEG-based biometric recognition is to perform person identification (PI) through EEG signals collected by performing motor imagination and visual evoked tasks. The aim of this paper is to improve the performance of different affective EEG-based PI using a channel attention mechanism of convolutional neural dense connection network (CADCNN net) approach. Channel attention mechanism (CA) is used to handle the channel information from the EEG, while convolutional neural dense connection network (DCNN net) extracts the unique biological characteristics information for PI. The proposed method is evaluated on the state-of-the-art affective data set HEADIT. The results indicate that CADCNN net can perform PI from different affective states and reach up to 95%-96% mean correct recognition rate. This significantly outperformed a random forest (RF) and multilayer perceptron (MLP). We compared our method with the state-of-the-art EEG classifiers and models of EEG biometrics. The results show that the further extraction of the feature matrix is more robust than the direct use of the feature matrix. Moreover, the CADCNN net can effectively and efficiently capture discriminative traits, thus generalizing better over diverse human states.
ISSN:1939-0114
1939-0122
DOI:10.1155/2021/7568460