Performance Enhancement in P300 ERP Single Trial by Machine Learning Adaptive Denoising Mechanism

Haider, Syed Kamran and Jiang, Aimin and Jamshed, Muhammad Ali and Pervaiz, DR Haris and Mumtaz, Shahid (2018) Performance Enhancement in P300 ERP Single Trial by Machine Learning Adaptive Denoising Mechanism. IEEE Networking Letters. ISSN 2576-3156

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Networking Letters
Additional Information:
©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.
ID Code:
130702
Deposited By:
Deposited On:
25 Jan 2019 10:20
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Oct 2020 05:50