Identification of Students’ Confusion in Classes from EEG Signals using Convolution Neural Network

Abstract

For a student, classes are vital factors for gaining knowledge. The lectures may be online or offline, but getting knowledge without confusion is a major issue. Confusion of students can be rectified after knowing that students are suffering from confusion and the confusion labels can be measured from the electroencephalography signals of the students. Machine learning approaches were implemented on electroencephalography signals to identify the suffering of students from confusion. The performance of traditional
machine learning approaches in predicting confusion status is found as poor. The one-dimensional convolution neural network is implemented on the electroencephalography signals of students, when they were watching video classes, to detect confusion in
the students. Students’ attention, mediation, electroencephalography signals frequency, delta, theta, alpha1, alpha2, beta1, beta2, gamma1 and gamma2 are generated from electroencephalography signals and are taken into consideration to training one-dimensional convolution neural network classifier and have achieved a better accuracy in detecting the confusion of the students. Besides finding confusion label of students, when understandable classes are creating confusion and difficult classes is understandable by students are identified from electroencephalography signals. This identification can help for improving students’ deficiencies by examining and treating. For future work, more data and different aspects of the students can be taken into consideration for detecting confusion and different obstacles to having perfect knowledge from the classes.