Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities

Abstract

This study analyzes the learning styles of subjects based on their electroencephalo-graphy (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 (eyes open and eyes closed conditions) and when performing learning tasks. For this purpose, 34 healthy subjects are recruited. The subjects have no background knowledge of the animated learning content. The subjects are shown the animated learning content in a video format. The experiment consists of two sessions and each session comprises two parts: (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 min, and 2 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 PSD and DWT features are analyzed. The EEG PSD and DWT features are computed for the recorded EEG in the alpha and gamma frequency bands 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 neighbor ( k -NN) classifier using the Mahalanobis distance metric, with 10-fold cross validation and support vector machine (SVM) classifier using linear kernel, with 10-fold cross validation. The classification results showed 97% and 94% accuracies rate for the first session and 96% and 93% accuracies for the second session in the alpha and gamma bands for the visual learners and non-visual learners, respectively, for k -NN classifier for PSD features and 68% and 100% accuracies rate for first session and 100% accuracies rate for second session for DWT features using k -NN classifier for the second session in the alpha and gamma band. For PSD features 97% and 96% accuracies rate for the first session, 100% and 95% accuracies rate for second session using SVM classifier and 79% and 82% accuracy for first session and 56% and 74% accuracy for second session for DWT features 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.

BED: A New Data Set for EEG-Based Biometrics

“…3) Spectral Features (SPEC): Four spectral features were extracted from the commonly used [79] θ (4-8 Hz), β (12-30 Hz), γ (30+ Hz), and α (8-12 Hz) bands of each of the EEG signal's channels. The four computed spectral features were the spectral centroid, spectral bandwidth, spectral crest factor, and spectral flatness, and were computed as proposed in [80].…”

Cognitive and Affective Brain–Computer Interfaces for Improving Learning Strategies and Enhancing Student Capabilities: A Systematic Literature Review

“…These researchers have used assessment method (Kolb's Learning Style Inventory (KLSI), [134]) for the measurement of learning style to understand the preferred way of learning [135]- [138]. Two publications about learning styles [112], [139] have been published in SCImago journals (Journal of Engineering and Applied Sciences and Frontiers in Behavioral Neuroscience). Both of these publications have claimed that learning styles can be measured from the brain activity using EEG measurement.…”

“…Both of these publications have claimed that learning styles can be measured from the brain activity using EEG measurement. For instance, Jawed et al [112] have classified students based on visual or non-visual learning styles. The students watched the video and/or hear the audio during the testbed to recall the memory.…”

Classifying Creativity: Applying Machine Learning Techniques to Divergent Thinking EEG Data

“…The growing use of machine learning in EEG research is at least partially attributable to the increased availability of code and software packages, as well as the ever-increasing computational capacity of modern computers, without the need to purchase a machine that can fill a small room. The purposes of studies of this nature have varied widely, and include attempts to detect disease (Orrù, Pettersson-Yeo, Marquand, Sartori, & Mechelli, 2012), recognize emotional states (Giannakaki, Giannakakis, Farmaki, & Sakkalis 2017), and even classify individuals by learning style (Jawed, Amin, Malik, & Faye, 2019). In the case of brain-computer interface (BCI) applications, motor cortical patterns have been classified and used to control virtual and even physical objects (Abiri, Borhani, Sellers, Jiang, & Zhao, 2018).…”