A Learning State Monitor Based on the Entropy of EEG Signal Samples

This paper designs and implements a set of wearable learning status monitor based on EEG, and systematically expounds the overall design scheme, hardware and software. Through the acquisition and denoising of EEG signals, the learning fatigue state is displays in real time on the terminal through bl...

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Bibliographic Details
Published in2020 5th International Conference on Computer and Communication Systems (ICCCS) pp. 675 - 679
Main Authors Fang, Yunjie, Hou, Qiaodan, Li, Weihao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2020
Subjects
Biomedical monitoring
EEG
Electrodes
Electroencephalography
Entropy
extreme learning machine
Feature extraction
Monitoring
sample entropy
wavelet packet decomposition
Wavelet packets
wearable collector
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Summary:This paper designs and implements a set of wearable learning status monitor based on EEG, and systematically expounds the overall design scheme, hardware and software. Through the acquisition and denoising of EEG signals, the learning fatigue state is displays in real time on the terminal through bluetooth transmission, signal preprocessing, feature extraction and classification. Experiments showed that The eeg monitor was 86.2% accurate in measuring students' attention and 75.4% accurate in measuring their emotional state, and is portable and wearable, providing a powerful technical support for improving learning quality in time.
DOI:10.1109/ICCCS49078.2020.9118550