GA-CNN: Analyzing student’s cognitive skills with EEG data using a hybrid deep learning approach

Abstract

A cognitive state (CS) assessment can be effectively performed using Electroencephalogram (EEG). However, due to the curse of dimensionality issues of EEG, most of the clustering methods often lead to poor performance. Deep neural network-based representation learning transforms high-dimensional data into lower-dimensional feature space, increasing the performance of CS prediction in students. So, in this research, a novel graph-based conventional attention neural network (GA-CNN) is developed to reduce distribution differences while analyzing student performance. To perform this classification, the obtained EEG signal dataset is denoised with the help of Stationary Wavelet Transform (SWT) based Independent Component Analysis (ICA) that filters out the noise signals. Likewise, the behavioural dataset is pre-processed using zero normalization and Not a Number (NaN) value computation method. Then, the effective features are extracted from the pre-processed data using the Long-Short-Term-Memory (LSTM) technique. Finally, the GA-CNN model is initiated to classify the students’ cognitive state (CS). The proposed method is implemented using the MATLAB tool, and the performance of the expected GA-CNN model is compared to other approaches where the analysis is done using the benchmark Kaggle EEG data set. The classification accuracy is significantly improved compared to other methods. The model achieves 87% accuracy, 98% precision, 75% recall, and 85% F1 score, outperforming various methods and making a better compromise.