Classification of Confusion Level Using EEG Data and Artificial Neural Networks

Abstract

the purpose of this study is to create an artificial neural network (ANN) that can classify a person’s level of confusion using Electroencephalography (EEG) data, more specifically, using the power spectrum of all the brain wave frequencies. This could help people in understanding the complicated mechanisms present in the brain, including the role that each specific brain wave signal plays in the formation of different cognitive activities in one’s mind such as confusion and workload. This study is categorized as a cognitive-affective state research, inspired by its current possible application to different existing societal fields such as education and gaming industries. The processing platforms used to process and interpret the dataset used in this research are Microsoft Excel and MATLAB software, applying frequency-based analysis and standard averaging methods fit for EEG data classification and artificial neural network modeling.