Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities - Jawed, Soyiba; Amin, Hafeez Ullah; Malik, Aamir Saeed; Faye, Ibrahima (Frontiers in Behavioral Neuroscience, 2019)
This study analyzes the learning styles of subjects based on their Electroencephalography (EEG) signals. The goal is to identify how the EEG features of a visual learner differ from those of a non-visual learner. The idea is to measure the students’ EEGs during the resting states and when performing learning tasks and retrieval tasks. Thirty-four healthy subjects are recruited. The subjects have no background knowledge of the content. The subjects are shown the animated learning content in a video format. The experiment consists of two sessions: 1) Learning task: the subjects are shown the animated learning content for an 8-10 min duration. 2) Memory retrieval task: The EEG signals are measured during the leaning task and memory retrieval task in two sessions. The retention time for the first session was 30 minutes, and two months for the second session. The analysis is performed for the EEG measured during the memory retrieval tasks. The study characterizes and differentiates the visual learners from the non-visual learners considering the extracted EEG features, such as the power spectral density (PSD), power spectral entropy (PSE) and Discrete wavelet transform (DWT). The EEG PSD and DWT features are computed for the recorded EEG over 128 scalp sites. The alpha and gamma frequency band for frontal, occipital and parietal regions are analyzed as these regions are activated during learning. The extracted PSD and DWT features are then reduced to 8 and 15 optimum features using principal component analysis (PCA). The optimum features are then used as an input to the k-nearest neighbour (k-NN) classifier and Support vector machine (SVM) classifier, with 10 -fold cross-validation. The classification results showed 97%, 94% ,96% and 93% accuracies for the first and second session respectively in the PSD alpha and gamma bands for the visual learners and non-visual learners. PSD features showed 97% and 96%, and100% and 95% accuracies rate for the first and second session respectively using SVM classifier. The results showed that the PSDs in the alpha and gamma bands represent distinct and stable EEG signatures for visual learners and non-visual learners during the retrieval of the learned contents.