Augmented Lagrangian method for TV-l 1 -l 2 based colour image restoration
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Publication Details
Author list: Padcharoen A., Kumam P., Martํnez-Moreno J.
Publisher: Institute of Electrical and Electronics Engineers Inc.
Publication year: 2019
Volume number: 354
Issue number: 3
Start page: 507
End page: 519
Number of pages: 13
ISSN: 2379-8920
eISSN: 2379-8920
Languages: English-Great Britain (EN-GB)
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Abstract
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 mental states (affective EEG). The aim of this study is to improve the performance of affective EEG-based PI using a deep learning approach. We proposed a cascade of deep learning using a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). CNNs are used to handle the spatial information from the EEG while RNNs extract the temporal information. We evaluated two types of RNNs, namely, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The proposed method is evaluated on the state-of-the-art affective dataset DEAP. The results indicate that CNN-GRU and CNN-LSTM can perform PI from different affective states and reach up to 99.90–100% mean Correct Recognition Rate. This significantly outperformed a support vector machine baseline system that used power spectral density features. Notably, the 100% mean CRR came from 32 subjects in DEAP dataset. Even after the reduction of the number of EEG electrodes from thirty-two to five for more practical applications, the model could still maintain an optimal result obtained from the frontal region, reaching up to 99.17%. Amongst the two deep learning models, we found that CNN-GRU and CNN-LSTM performed similarly while CNN-GRU expended faster training time. In conclusion, the studied DL approaches overcame the influence of affective states in EEG-Based PI reported in the previous works. IEEE
Keywords
Biometrics, Convolutional neural networks, Electroencephalography, Long Short-term memory, Personal identification, Recurrent neural networks.
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