Study on Detection of P300 ERP Component in EEG Signals and Algorithms

DOI

10.2991/eeeis-17.2017.49How to use a DOI?

Keywords

component; Lie detection, P300, extreme learning machine, event related potential.

Abstract

Abstract.P300 Event Related Potential (ERP) has drawn much interest in different application of psychological background. An endogenous ERP component, P300, has been widely inspected in detecting deception. Detecting P300 from EEG signals is hard to find because P300 is overloaded with noise and other EEG signals. We typically use the features of the amplitude and latency of P300 to recognize P300.In this paper we are presenting several methods for the efficient detection of P300 detection algorithms such as; reconstruction algorithm for small P300 wave using Independent Component Analysis (ICA), Extreme Learning Machine (ELM) method using Back-Propagation network, Multi-domain EEG signal processing. Each detection method has good effectiveness but the complexation level is dissimilar for each algorithm based on the constraints and optimal features used at the classification stage. This survey will brings you algorithms for the P300 detection and give you detail idea about the use of them.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Syed Kamran Haider
AU  - Ai-Min JIANG
AU  - Shahzad Ashraf
AU  - Inam Ullah
PY  - 2017/09
DA  - 2017/09
TI  - Study on Detection of P300 ERP Component in EEG Signals and Algorithms
BT  - Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2017)
PB  - Atlantis Press
SP  - 353
EP  - 359
SN  - 2352-5401
UR  - https://doi.org/10.2991/eeeis-17.2017.49
DO  - 10.2991/eeeis-17.2017.49
ID  - Haider2017/09
ER  -