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

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.