Investigation of the neural correlation with task performance and its effect on cognitive load level classification

Electroencephalogram (EEG)-based cognitive load assessment is now an important assignment in psychological research. This type of research work is conducted by providing some mental task to the participants and their responses are counted through their EEG signal. In general assumption, it is consid...

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Bibliographic Details
Published in:PloS one 2023-12, Vol.18 (12), p.e0291576-e0291576
Main Authors: Khanam, Farzana, Ahmad, Mohiuddin, Hossain, A B M Aowlad
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Biology and Life Sciences
Brain research
Classification
Cognition
Cognitive load
Cognitive science
Cognitive tasks
Computer and Information Sciences
Datasets
EEG
Electroencephalography
Evaluation
Hypotheses
Investigations
Machine learning
Medicine and Health Sciences
Memory
Neural networks
Neurons
Physical Sciences
Popularity
Research and Analysis Methods
Statistical models
Support vector machines
Workloads
Citations: Items that this one cites
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Summary:Electroencephalogram (EEG)-based cognitive load assessment is now an important assignment in psychological research. This type of research work is conducted by providing some mental task to the participants and their responses are counted through their EEG signal. In general assumption, it is considered that during different tasks, the cognitive workload is increased. This paper has investigated this specific idea and showed that the conventional hypothesis is not correct always. This paper showed that cognitive load can be varied according to the performance of the participants. In this paper, EEG data of 36 participants are taken against their resting and task (mental arithmetic) conditions. The features of the signal were extracted using the empirical mode decomposition (EMD) method and classified using the support vector machine (SVM) model. Based on the classification accuracy, some hypotheses are built upon the impact of subjects' performance on cognitive load. Based on some statistical consideration and graphical justification, it has been shown how the hypotheses are valid. This result will help to construct the machine learning-based model in predicting the cognitive load assessment more appropriately in a subject-independent approach.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0291576