Investigation of the effect of rosemary odor on mental workload using EEG: an artificial intelligence approach

Mental load is the load that occurs on the brain during a cognitive activity. Excessive increase in mental load causes a decrease in the success of the work or the inability to do the work. In this study, the effect of rosemary essential odor on mental load was evaluated with the Stroop test. The ai...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2022-03, Vol.16 (2), p.497-504
Main Authors: Şahin Sadık, Evin, Saraoğlu, Hamdi Melih, Canbaz Kabay, Sibel, Tosun, Mustafa, Keskinkılıç, Cahit, Akdağ, Gönül
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Computer Imaging
Computer Science
Decision trees
Electroencephalography
Evaluation
Feature extraction
Image Processing and Computer Vision
Multimedia Information Systems
Odors
Original Paper
Pattern Recognition and Graphics
Regression analysis
Signal,Image and Speech Processing
Smell
Test cards
Vision
Workload
Workloads
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Summary:Mental load is the load that occurs on the brain during a cognitive activity. Excessive increase in mental load causes a decrease in the success of the work or the inability to do the work. In this study, the effect of rosemary essential odor on mental load was evaluated with the Stroop test. The aim of the study is to show that the mental load during the Stroop test is reduced in volunteers who smell rosemary odor and to examine the effect of rosemary odor on the Stroop test. When the Stroop test results were evaluated statistically, the Stroop test task has completed an average of 1 second faster for each card in the presence of rosemary odor and the time responses were examined with the help of the one-way ANOVA test. Electroencephalogram (EEG) signals of 30 volunteers were collected in the study. For each volunteer, spectral features in 4 sub bands (delta, theta, alpha and beta) were extracted from the EEG signal using the Welch method, and the most significant features were selected from the extracted features using the decision tree feature importance method. The data were separated as 80% training and 20% testing, and with the help of the classification and regression tree algorithm, the mental workload created by the Stroop test cards on the volunteers could be classified with an accuracy of 96.88%.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-021-01992-5