การศึกษาเปรียบเทียบคลื่นไฟฟ้าสมองของกลุ่มนิสิตที่มีภาวะโรคซึมเศร้าและนิสิตทั่วไปในขณะพัก

Abstract

This engineering project is a study and comparative analysis of brain signals between students with depression and students without depression in resting state. The projected signal differentiation analysis was initiated by measuring the participants' brain signals and analyzing the differences by modeling through a deep learning program. The statistical difference of brain signals of the students with depression and the normal students. It was determined compare with the PHQ-9 Depression Test for the trends in brainwave signal analysis and test scores. From the experiment, the results were summarized which can be divided into 2 parts as follows Part 1: The test's results Level 1 had no participants with a predisposition to depression; Level 2 had 5 participants with mild depression; Level 3 had 4 participants with moderate depression, Level 4 had 2 participants with moderately severe depression, and Level 5 had 1 participant with severe depression. Part 2: The results were analyzed by a deep learning program, which trained through datasets from measuring brain signals. Following that, efficacy was assessed and compared. It was found that when the condition was opened and closed eyes for 1 minute each (easel 1), the accuracy of signal classification was between 60% and 100%. It was used to measure brainwaves that are more accurate than other conditions. However, the accuracy of results may decrease as the number of data sets used for training were too small. In addition, the research results may change according to the information used in the training program.0yfsk