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

卷积神经网络 人工智能 计算机科学 深度学习 脑电图 机器学习 鉴定(生物学) 模式识别(心理学) 视觉学习 特征(语言学) 特征提取 可视化 人工神经网络 语音识别 心理学 生物 植物 精神科 发展心理学 哲学 语言学
作者
Soyiba Jawed,Ibrahima Faye,Aamir Saeed Malik
出处
期刊:IEEE Transactions on Neural Systems and Rehabilitation Engineering [Institute of Electrical and Electronics Engineers]
卷期号:32: 378-390
标识
DOI:10.1109/tnsre.2024.3351694
摘要

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.
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