Subject Dependent Cognitive Load Level Classification from fNIRS Signal Using Support Vector Machine

Ayman, Syeda Umme; Arrafuzzaman, Al; Rahman, Md. Asadur

doi:10.1007/978-981-19-7528-8_29

Cited by 1 publication

(1 citation statement)

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“…The EEG data were classified by three deep learning subject-specific classifiers: convolutional neural network (CNN), long short-term memory network (LSTM), and convolutional LSTM network (Conv-LSTM) and reached the best accuracy of 97.92% from feature combination of phase locking value with CNN and Conv-LSTM. Recent work [ 26 ] utilized the SVM model to classify the two classes (high-low) of cognitive workload ( 0 -back vs 2 -back & 3 -back) with the highest accuracy of 94.44% and an average accuracy of 75.85% for a 50:50 train test ratio.…”

Section: Introductionmentioning

confidence: 99%

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

Khanam,

Ahmad,

Hossain

2023

PLoS ONE

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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.

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Section: Introductionmentioning

confidence: 99%