A study on reward and punishment learning using a data-driven approach

Abu Md. Sadat, Farhana Binta Salim, Maria Islam Ema, Anita Mahmud Jhara, Mohammad Zavid Parvez, Md Anisur Rahman

Research output: Book chapter/Published conference paperConference paperpeer-review

Abstract

Mental stress is the main well-being problem world-wide today. It is responsible for most of all mental-brain diseases. Mental stress does not need any specific reason to happen. It can be experienced from a very little incident to a huge issue. The consequence of it depends on how people handle it. Depression and anxiety are one of the results of it, and they are one of the major challenges in today’s world, and sometimes depression and anxiety patients are taking major steps like suicide. Additionally, most of the time, suicidal patients hide their true feelings and fail to communicate their psychiatric problems to physicians. The specific issues that need to be addressed are finding an easy, reliable and realistic way to diagnose mental stress to keep it from becoming a serious and irreversible condition. The primary prevention of mental stress utilizing machine learning algorithms based on reward and punishment processing is important to avoid mental diseases. Several techniques have been used to detect mental stress, and very few papers have tried to detect a patients’ comorbidity condition. However, literature shows that there are still chances of further improvement in this field. The traditional methods of detecting Mental stress involve a statistical questionnaire approach with some shortcomings as they are easy to fake, which is not possible if we use EEG signals. Therefore, in this paper, we proposed a method to evaluate the Electroencephalogram (EEG) signals on thirty-two individuals for identifying co morbid patients using nine Machine Learning classifiers based on reward and punishment processing. The performance of our method is also shown to be better than some existing methods.
Original languageEnglish
Title of host publication2021 IEEE international conference on Systems, Man, and Cybernetics (SMC)
Place of PublicationUnited States
PublisherIEEE Xplore
Pages2381-2388
Number of pages8
ISBN (Electronic)9781665442077
ISBN (Print)9781665442084 (Print on demand)
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 - Melbourne Convention Centre and virtual, Melbourne, Australia
Duration: 17 Oct 202120 Oct 2021
http://ieeesmc2021.org/
http://ieeesmc2021.org/call-for-papers/ (Call for papers)
https://ieeexplore-ieee-org.ezproxy.csu.edu.au/xpl/conhome/9658572/proceeding (Proceedings)

Conference

Conference2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Country/TerritoryAustralia
CityMelbourne
Period17/10/2120/10/21
OtherThe 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2021), is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers, educators and practitioners to learn, share knowledge, report most recent innovations and developments, and to exchange ideas and advances in all aspects of systems science and engineering, human-machine systems, and cybernetics.
Internet address

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