Advancements in Communication and Systems
Artificial Intelligence Technology Based on EEG Signals for Monitoring the Online Education Industry
Authors: Vinay Kumar Singh, Shiv Prakash, Ram Bhushan and Krishna Nand Mishra
Publishing Date: 13-09-2024
ISBN: 978-81-955020-7-3
Abstract
A wearable Neurosky device that uses learner EEG data in real-time to detect attentiveness during online learning. An adaptive learning environment can control low cognitive and mental attention duration by responding to learner requests. The student's level of involvement has a substantial impact on online learners' propensity to study. The efficacy of the proposed method is explicitly evaluated based on its classification accuracy in predicting engagement. The online teaching approach can be automatically changed by modifying the content and communication approaches when distraction, disinterest, or superficial involvement occurs during an online session with the learner. The novel technique involves acquiring EEG data, focusing specific attention on movements, eye blinks, and facial expressions at frequencies linked to concentration or attention. An AI and machine learning technique based on EEG data is used to predict learner attention. A government or independent organization employs this technology to monitor and control the online education sector.
Keywords
EEG, Machine learning, SVM, AI
Cite as
Vinay Kumar Singh, Shiv Prakash, Ram Bhushan and Krishna Nand Mishra, "Artificial Intelligence Technology Based on EEG Signals for Monitoring the Online Education Industry", In: Ashish Kumar Tripathi and Vivek Shrivastava (eds), Advancements in Communication and Systems, SCRS, India, 2024, pp. 521-527. https://doi.org/10.56155/978-81-955020-7-3-46