Electroencephalogram signal recording and processing for subjects’ engagement analysis with visual content

Published: 12 September 2022| Version 1 | DOI: 10.17632/ztg5jnnwpr.1
Contributors:
Rahul Upadhyay,
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Description

Since attention affects cognitive performance, it is essential to identify and keep track of students' attention during learning. The ability of interactive learning systems to modify tutoring content, provide efficient help strategies, and enhance learning outcomes may thus be made possible by getting a detailed understanding of a learner's mental state. In computer-based learning environments, keeping track of students' mental states is vital.  This research is an investigation into the viability of using active learning to enhance student engagement index when exposed to varied visual stimuli. The study involves gathering EEG data from twenty participants (ten men and ten women) while they rest and interact with various virtual learning tools. The Allengers Neuro PLOT, a 28-channel wet electrode system, was used to collect the EEG data. EEG data under resting and audio-visual stimuli, both raw and pre-processed, are included in the work that follows. The research community will have access to the recorded data, which is supported by an advanced EEG data pre-processing pipeline. Note: The recorded EEG data uploaded here are referred to by the following abbreviations: Sub: Subject N: Non-Processed/Raw Data P: Processed Data BL: Baseline IT: Infotainment ET: Entertainment AL: Active Learning EC: Educational Content AP: Absolute Power RP: Relative Power Spect: Spectral Image Example: Sub1N_ET: Subject 1, Non-Processed, ET: Entertainment

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Categories

Neuroscience, Health Sciences, Biomedical Engineering, Mental Health, Active Learning, Electroencephalography

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