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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Performance Enhancement in P300 ERP Single Trial by Machine Learning Adaptive Denoising Mechanism
AU - Haider, Syed Kamran
AU - Jiang, Aimin
AU - Jamshed, Muhammad Ali
AU - Pervaiz, DR Haris
AU - Mumtaz, Shahid
N1 - ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2018/11/28
Y1 - 2018/11/28
N2 - The P300-based lie detection scheme is yet another and advantageous tactic for unadventurous Polygraphy. In the proposed scheme, the raw electroencephalogram (EEG) signals are assimilated from 15 subjects during deception detection. After the assimilation, EEG signals are separated using an independent component analysis (ICA). The proposed adaptive denoising approach, extracts three kinds of features from denoised wave to reproduce P300 waveform and identify the P300 components at the Pz electrode. Finally, in order to enhance the performance, four classifiers are used, i.e., support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbor (KNN), and back propagation neural network (BPNN), achieving the accuracy of 74.5%, 79.4%, 97.9% and 89%, respectively.
AB - The P300-based lie detection scheme is yet another and advantageous tactic for unadventurous Polygraphy. In the proposed scheme, the raw electroencephalogram (EEG) signals are assimilated from 15 subjects during deception detection. After the assimilation, EEG signals are separated using an independent component analysis (ICA). The proposed adaptive denoising approach, extracts three kinds of features from denoised wave to reproduce P300 waveform and identify the P300 components at the Pz electrode. Finally, in order to enhance the performance, four classifiers are used, i.e., support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbor (KNN), and back propagation neural network (BPNN), achieving the accuracy of 74.5%, 79.4%, 97.9% and 89%, respectively.
U2 - 10.1109/LNET.2018.2883859
DO - 10.1109/LNET.2018.2883859
M3 - Journal article
JO - IEEE Networking Letters
JF - IEEE Networking Letters
SN - 2576-3156
ER -