Enhancing the feasibility of cognitive load recognition in remote learning using physiological measures and an adaptive feature recalibration convolutional neural network.
Med Biol Eng Comput
; 60(12): 3447-3460, 2022 Dec.
Article
in English
| MEDLINE | ID: covidwho-2048496
ABSTRACT
The precise assessment of cognitive load during a learning phase is an important pathway to improving students' learning efficiency and performance. Physiological measures make it possible to continuously monitor learners' cognitive load in remote learning during the COVID-19 outbreak. However, maintaining a good balance between performance and computational cost is still a major challenge in advancing cognitive load recognition technology to real-world applications. This paper introduced an adaptive feature recalibration (AFR) convolutional neural network to overcome this challenge by capturing the most discriminative physiological features (EEG and eye-tracking). The results revealed that the optimal average classification accuracy of the feature combination obtained by the AFR method reached 95.56% with only 60 feature dimensions. Additionally, compared with the best result of the conventional correlation-based feature selection (CFS) method, the introduced AFR algorithm achieved higher accuracy and cheaper computational cost, as well as a 2.06% improvement in accuracy and a 51.21% reduction in feature dimension, which is more in line with the requirements of low delay and real-time performance in practical BCI applications.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Electroencephalography
/
COVID-19
Limits:
Humans
Language:
English
Journal:
Med Biol Eng Comput
Year:
2022
Document Type:
Article
Affiliation country:
S11517-022-02670-5
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