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

Abstract

Mental stress is the main well-being problem worldwide today. It is responsible for most of all mental-brain diseases. It does not need any specific reason to happen. It can be experienced from a very little incident to a huge incident. The consequence of it depends on how people handle it. Depression and anxiety are the results of mental stress, and they are the major challenges in today's world. Depression and anxiety are the leading causes of suicide. 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 results based on the traditional approach can be biased, which is not possible if Electroencephalogram (EEG) signals are used. Therefore, in this paper, we proposed a method to evaluate the EEG signals on thirty-two individuals for identifying comorbid 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.