1. Estimating cognitive load during mental arithmetic task using EEG signal.
- Author
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Gunda, Manasa, Nirde, Krashana. D., Gajre, Suhas. S., and Manthalkar, Ramchandra
- Subjects
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COGNITIVE load , *DECOMPOSITION method , *MACHINE learning , *SIGNAL processing , *ELECTROENCEPHALOGRAPHY , *MENTAL arithmetic - Abstract
Cognitive load on the brain is induced by employing cognitive tasks of varying degrees. The load is measured by using the recorded EEG signal while performing the cognitive task. The EEG signals are processed to determine the complexity of the task. The current study looks at different Machine learning Classification techniques to classify 2 tasks. Six pairs of EEG electrodes are arranged in a 10-20 system to capture the EEG signals. The pairs are located on different lobe areas namely pre-frontal, frontal, parietal, temporal, central, occipital. The paper uses the decomposition method like biorthogonal wavelet basis for extracting features from bands. Different Machine Learning classifiers are implemented on two methods like Individual brain lobe areas and Pair-wise electrode combination. Four features like band power, mean, energy and relative energy are extracted and applied as input to classifier for classification of 2 tasks i.e. resting state and mental arithmetic tasks. In Individual brain lobe area method frontal and parietal area have achieved highest classification accuracy of 73.5%. In Pair-wise electrode combination, 3-pair combination (average of all the classifier) i.e., prefrontal, frontal, central have achieved good classification accuracy of 66%. In EEG band ratios indices, band power as feature used to extract different EEG band ratios for pre-frontal and frontal electrodes in L/R (left/right hemisphere), R/L (right/left hemisphere) and (L-R)/(L+R) ratios. The energy in different frequency bands is used to quantify the cognitive load. We conclude from this study that band power ratios of right lobe to left lobe (R/L) perform better in assessing the cognitive load. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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