Deep learning-based assessment model for Real-time identification of visual learners using Raw EEG

Automatic identification of visual learning style in real time using raw
electroencephalogram (EEG) is challenging. In this work, inspired by the powerful
abilities of deep learning techniques, deep learning-based models are proposed to
learn high-level feature representation for EEG visual learning identification.
Existing computer-aided systems that use electroencephalograms and machine
learning can reasonably assess learning styles. Despite their potential, offline
processing is often necessary to eliminate artifacts and extract features, making
these methods unsuitable for real-time applications. The dataset was chosen with
34 healthy subjects to measure their EEG signals during resting states (eyes open
and eyes closed) and while performing learning tasks. The subjects displayed no
prior knowledge of the animated educational content presented in video format.
The paper presents an analysis of EEG signals measured during a resting state
with closed eyes using three deep learning techniques: Long-term, short-term
memory (LSTM), Long-term, short-term memory-convolutional neural network
(LSTM-CNN), and Long-term, short-term memory - Fully convolutional neural network
(LSTM-FCNN). The chosen techniques were based on their suitability for real-time
applications with varying data lengths and the need for less computational time.
The optimization of hypertuning parameters has enabled the identification of
visual learners through the implementation of three techniques. LSTM- CNN
technique has the highest average accuracy of 94%, a sensitivity of 80%,
a specificity of 92%, and an F1 score of 94% when identifying the visual learning
style of the student out of all three techniques. This research has shown that
the most effective method is the deep learning-based LSTM-CNN technique, which
accurately identifies a student's visual learning style.