Electroencephalogram Data Collection for Student Engagement Analysis with Audio-Visual Content
Upadhyay, R.; Singh, M.; Mishra, S.; Mehta, J.; Bansal, A.; Baranwal, M.; Kumar, V.
Show abstract
Recognizing and monitoring students attention during learning is crucial to successful knowledge acquisition since it influences cognitive function. As a result, gaining a precise picture of a learners mental state may enable interactive learning systems to alter tutoring content, devise effective help tactics, and improve learning outcomes. In computer-based learning environments, keeping track of students mental states is vital. Investigating the feasibility of utilizing active learning in enhancing student engagement index when exposed to various visual stimuli is the genesis of this work. The research includes collecting EEG data from 20 participants (ten males, ten females) while resting and being subjected to various virtual infotainment/educational content. The EEG data were collected using the Allengers Neuro PLOT, a 40-channel wet electrode system. The work includes raw and pre-processed EEG data under quiescent and audio-visual continuous cues. The recorded data is accommodated by a sophisticated EEG data pre-processing pipeline and will be available to the research community for usage. Specifications Table O_TBL View this table: org.highwire.dtl.DTLVardef@340852org.highwire.dtl.DTLVardef@e5c51org.highwire.dtl.DTLVardef@cefb1borg.highwire.dtl.DTLVardef@c7d208org.highwire.dtl.DTLVardef@ae4824_HPS_FORMAT_FIGEXP M_TBL C_TBL
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- EEG in game user analysis: A framework for expertise classification during gameplay 98%
- Alpha Neurofeedback Training with a portable Low-Priced and Commercially Available EEG Device Leads to Faster Alpha Enhancement.A Single-blind, Sham-feedback Controlled Study & Methodological Review 97%
- Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments 96%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Classification of complex emotions using EEG and virtual environment: proof of concept and therapeutic implication 95%
- Brain Functional Connectivity Correlates of Anomalous Interaction Between Sensorily Isolated Monozygotic Twins 95%
- Gamma Music: A New Acoustic Stimulus for Gamma-frequency Auditory Steady-State Response 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.