Cheating Detection in E-exams System Using EEG Signals

Abstract

Cheating in e-exams is a real problem that threatens academic integrity and underminesconfidence in the feasibility of remote assessments. Many previous research papers and studies discussedthe issue of cheating in e-exams to prevent or reduce it through the use of the available technologies suchas the use of a web camera to monitor the examinee, some researchers proposed using specific software torestrict the examinee from accessing resources that are not permitted during the exam. This work aims todesign a Semi-automatic, AI-based e-proctoring system that mitigates cheating in e-exams. This researchproposed an innovative method to detect the possibility of cheating in the e-exams. This method relies onthe use of IoT and the Muse2 devices to detect the examinee's physiological state and determine whether itis “Normal” or “Abnormal” through the examinee`s EEG signal, where the abnormal state indicates apossibility of cheating. Convolutional Neural Network (CNN) was used to distinguish the examinee's state.The collected data from 15 students at the fourth stage of the Computer Engineering Department/ Universityof Mosul ranging between 23 and 26 years old showed that there is an obvious difference between the“calm” or “Normal” state and “stress” or “Abnormal” state in the EEG signal of the volunteer. The accuracyof the system was obtained for many testing datasets. The dataset was divided into two main datasets; the30 and 60 seconds duration time. The best accuracy obtained for the 30sec duration time was 97.37%, and97.14% for the 60sec duration time. The researchers concluded that the EEG signal contains a lot ofimportant information that can be utilized to detect the physiological state of the examinee and that theMuse2 device can be reliable to record the EEG signal.