Mental State Detection in Classroom Based on EEG Brain Signals - Natural Science

NS > Vol.11 No.11, November 2019

Natural Science

Volume 11, Issue 11 (November 2019)

ISSN Print: 2150-4091 ISSN Online: 2150-4105

Google-based Impact Factor: 0.74 Citations h5-index & Ranking

Mental State Detection in Classroom Based on EEG Brain Signals ()

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Download as PDF (Size: 828KB) PP. 315-322

DOI: 10.4236/ns.2019.1111034 797 Downloads 2,017 Views Citations

Author(s)

Yan Gong^1*, Samuel Xu^2*

Affiliation(s)

¹Nanjing Agricultural University, Nanjing, China.
²Concordia International School Shanghai, Shanghai, China.

ABSTRACT

The goal of this work is to identify human brain waves in different states non-invasively, and to distinguish them into different levels of mental states in order to provide immediate mental state feedback to a classroom instructor and maximize learning outcomes. In order to apply such knowledge, this project utilizes a commercial NeuroSky Mindwave Mobile EEG to collect brain signals, MATLAB to filter data, voltage thresholds to detect blinks, which are used in tandem with power spectral density (PSD) analysis in order to classify mental states. This knowledge can then be provided to a class instructor who can use it to maximize the learning experience for students.

KEYWORDS

Electroencephalogram, Education, Filter, Convolute, Power Spectral Density

Share and Cite:

Gong, Y. and Xu, S. (2019) Mental State Detection in Classroom Based on EEG Brain Signals. Natural Science, 11, 315-322. doi: 10.4236/ns.2019.1111034.

Cited by

[1]	On the utility of the p3 as a neuromarker of academic performance: a brief review
	Italian Journal of Educational Technology, 2022

[2]	Estimating attention level from blinks and head movement
	EPiC Series in Computing, 2021

[3]	FREQUENCY AND POWER ANALYSIS OF EEG THETA ACTIVITY DURING DIFFERENT MATHEMATICAL AND VERBAL TASK IN MALES
	2020

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