EEG Pattern Recognition using Brain-Inspired Spiking Neural Networks for Modelling Human Decision Processes

Z.G. Doborjeh; M. Doborjeh; N. Kasabov

doi:10.1109/IJCNN.2018.8489748

EEG Pattern Recognition using Brain-Inspired Spiking Neural Networks for Modelling Human Decision Processes

Z.G. Doborjeh, M. Doborjeh, N. Kasabov

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

Abstract

This paper proposes a method utilising spiking neural networks (SNN) for modelling, visualising and comparing the brain data under complex mental states. The method was applied to a cognitive task performed by 23 participants while they were making decision on a moral dilemma situation-related task. An SNN evolving spatiotemporal data architecture is used to learn and visualise the neural activity across different brain regions. The model developed allows for studying the patterns of electrical activity of neurons elicited during complex decision making processes such as moral-related tasks. This could be used for predictive analysis of various aspects of human behavior during decision making and for other related cognitive tasks.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
ISBN (Electronic)	978-1-5090-6014-6
DOIs	https://doi.org/10.1109/IJCNN.2018.8489748
Publication status	Published (in print/issue) - 12 Jun 2018

Keywords

EEG
decision making
pattern recognition

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/IJCNN.2018.8489748

Cite this

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title = "EEG Pattern Recognition using Brain-Inspired Spiking Neural Networks for Modelling Human Decision Processes",

abstract = "This paper proposes a method utilising spiking neural networks (SNN) for modelling, visualising and comparing the brain data under complex mental states. The method was applied to a cognitive task performed by 23 participants while they were making decision on a moral dilemma situation-related task. An SNN evolving spatiotemporal data architecture is used to learn and visualise the neural activity across different brain regions. The model developed allows for studying the patterns of electrical activity of neurons elicited during complex decision making processes such as moral-related tasks. This could be used for predictive analysis of various aspects of human behavior during decision making and for other related cognitive tasks.",

keywords = "EEG, decision making, pattern recognition",

author = "Z.G. Doborjeh and M. Doborjeh and N. Kasabov",

year = "2018",

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language = "English",

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AU - Doborjeh, Z.G.

AU - Doborjeh, M.

AU - Kasabov, N.

PY - 2018/6/12

Y1 - 2018/6/12

N2 - This paper proposes a method utilising spiking neural networks (SNN) for modelling, visualising and comparing the brain data under complex mental states. The method was applied to a cognitive task performed by 23 participants while they were making decision on a moral dilemma situation-related task. An SNN evolving spatiotemporal data architecture is used to learn and visualise the neural activity across different brain regions. The model developed allows for studying the patterns of electrical activity of neurons elicited during complex decision making processes such as moral-related tasks. This could be used for predictive analysis of various aspects of human behavior during decision making and for other related cognitive tasks.

AB - This paper proposes a method utilising spiking neural networks (SNN) for modelling, visualising and comparing the brain data under complex mental states. The method was applied to a cognitive task performed by 23 participants while they were making decision on a moral dilemma situation-related task. An SNN evolving spatiotemporal data architecture is used to learn and visualise the neural activity across different brain regions. The model developed allows for studying the patterns of electrical activity of neurons elicited during complex decision making processes such as moral-related tasks. This could be used for predictive analysis of various aspects of human behavior during decision making and for other related cognitive tasks.

KW - EEG

KW - decision making

KW - pattern recognition

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U2 - 10.1109/IJCNN.2018.8489748

DO - 10.1109/IJCNN.2018.8489748

M3 - Conference contribution

SN - 978-1-5090-6015-3

BT - Proceedings of the International Joint Conference on Neural Networks

ER -

EEG Pattern Recognition using Brain-Inspired Spiking Neural Networks for Modelling Human Decision Processes

Abstract

Keywords

UN SDGs

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