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

Signal, Image and Video Processing volume 16, pages 497–504 (2022)Cite this article

Abstract

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%.

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References

  1. Paas, F., Tuovinen, J.E., Tabbers, H., Van Gerven, P.W.: Cognitive load measurement as a means to advance cognitive load theory. Educ. Psychol. 38(1), 63 (2003)

    Article  Google Scholar 

  2. Wen, H., Sze, N., Zeng, Q., Hu, S.: Effect of music listening on physiological condition, mental workload, and driving performance with consideration of driver temperament. Int. J. Environ. Res. Public Health 16(15), 2766 (2019)

    Article  Google Scholar 

  3. Fu, V.X., Oomens, P., Kleinrensink, V.E.E. et al. The effect of preferred music on mental workload and laparoscopic surgical performance in a simulated setting (OPTIMISE): a randomized controlled crossover study. Surg Endosc (2020). https://doi.org/10.1007/s00464-020-07987-6

  4. Geethanjali, B., Adalarasu, K., Rajsekaran, R.: Impact of music on brain function during mental task using electroencephalography. World Acad. Sci. Eng. Technol. 66, 883 (2012)

    Google Scholar 

  5. Lawless, J.: Aromatherapy and the Mind. HarperCollins UK (2014)

  6. Diego, M.A., Jones, N.A., Field, T., Hernandez-Reif, M., Schanberg, S., Kuhn, C., Mcadam, V., Galamaga, R., Galamaga, M.: Aromatherapy positively affects mood, EEG patterns of alertness and math computations. Int. J. Neurosci. 96(3–4), 217 (1998). https://doi.org/10.3109/00207459808986469

    Article  Google Scholar 

  7. Battaglia, S.: The Complete Guide to Aromatherapy. International Centre of Holistic Aromatherapy (2003)

  8. Filiptsova, O., Gazzavi-Rogozina, L., Timoshyna, I., Naboka, O., Dyomina, Y., Ochkur, A.: The essential oil of rosemary and its effect on the human image and numerical short-term memory. Egypt. J. Basic Appl. Sci. 4(2), 107 (2017). https://doi.org/10.1016/j.ejbas.2017.04.002

    Article  Google Scholar 

  9. Sanders, C., Diego, M., Fernandez, M., Field, T., Hernandez-Reif, M., Roca, A.: EEG asymmetry responses to lavender and rosemary aromas in adults and infants. Int. J. Neurosci. 112(11), 1305 (2002). https://doi.org/10.1080/00207450290158214

    Article  Google Scholar 

  10. Filiptsova, O., Gazzavi-Rogozina, L., Timoshyna, I., Naboka, O., Dyomina, Y., Ochkur, A.: The effect of the essential oils of lavender and rosemary on the human short-term memory. Alex. J. Med. 54(1), 41 (2018). https://doi.org/10.1016/j.ajme.2017.05.004

    Article  Google Scholar 

  11. Moss, M., Cook, J., Wesnes, K., Duckett, P.: Aromas of rosemary and lavender essential oils differentially affect cognition and mood in healthy adults. Int. J. Neurosci. 113(1), 15 (2003). https://doi.org/10.1080/00207450390161903

    Article  Google Scholar 

  12. Abbasi, N.I., Bezerianos, A., Hamano, J., Chaudhury, A., Thakor, N.V., Dragomir, A.: Evoked brain responses in odor stimuli evaluation—an EEG event related potential study. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2020-July, p. 2861 (2020). https://doi.org/10.1109/EMBC44109.2020.9175650

  13. Lorig, T.S., Schwartz, G.E.: Brain and odor: I. Alteration of human EEG by odor administration. Psychobiology 16(3), 281 (1988). https://doi.org/10.3758/BF03327318

    Article  Google Scholar 

  14. Reis, P.M., Hebenstreit, F., Gabsteiger, F., von Tscharner, V., Lochmann, M.: Methodological aspects of EEG and body dynamics measurements during motion. Front. Hum. Neurosci. (2014). https://doi.org/10.3389/fnhum.2014.00156

    Article  Google Scholar 

  15. Berger, H.: Über das Elektrenkephalogramm des Menschen—Dritte Mitteilung. Arch. Psychiatr. Nervenkr. 94(1), 16 (1931). https://doi.org/10.1007/BF01835097

    Article  Google Scholar 

  16. Kroupi, E., Yazdani, A., Vesin, J.M., Ebrahimi, T.: EEG correlates of pleasant and unpleasant odor perception. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 11(1s), 1 (2014)

    Article  Google Scholar 

  17. Aydemir, O.: Olfactory recognition based on EEG gamma-band activity. Neural Comput. 29(6), 1667 (2017)

    Article  MathSciNet  Google Scholar 

  18. Hou, H.R., Zhang, X.N., Meng, Q.H.: Odor-induced emotion recognition based on average frequency band division of EEG signals. J. Neurosci. Methods 334(January), 108599 (2020). https://doi.org/10.1016/j.jneumeth.2020.108599

    Article  Google Scholar 

  19. Aarsland, D., Creese, B., Politis, M., Chaudhuri, K.R., Weintraub, D., Ballard, C.: Cognitive decline in Parkinson disease. Nat. Rev. Neurol. 13(4), 217 (2017)

    Article  Google Scholar 

  20. Kamei, S., Morita, A., Serizawa, K., Mizutani, T., Hirayanagi, K.: Quantitative EEG analysis of executive dysfunction in Parkinson disease. J. Clin. Neurophysiol. 27(3), 193 (2010)

    Article  Google Scholar 

  21. Rahayel, S., Frasnelli, J., Joubert, S.: The effect of Alzheimers disease and Parkinsons disease on olfaction: a meta-analysis. Behav. Brain Res. 231(1), 60 (2012)

    Article  Google Scholar 

  22. Li, F., Zhang, G., Wang, W., Xu, R., Schnell, T., Wen, J., McKenzie, F., Li, J.: Deep models for engagement assessment with scarce label information. IEEE Trans. Hum. Mach. Syst. 47(4), 598 (2017). https://doi.org/10.1109/THMS.2016.2608933

    Article  Google Scholar 

  23. Yin, Z., Zhang, J.: Cross-session classification of mental workload levels using EEG and an adaptive deep learning model. Biomed. Signal Process. Control 33, 30 (2017). https://doi.org/10.1016/j.bspc.2016.11.013

    Article  Google Scholar 

  24. Kardan, O., Adam, K.C., Mance, I., Churchill, N.W., Vogel, E.K., Berman, M.G.: Distinguishing cognitive effort and working memory load using scale-invariance and alpha suppression in EEG. Neuroimage 211(January), 116622 (2020). https://doi.org/10.1016/j.neuroimage.2020.116622

    Article  Google Scholar 

  25. Stroop, J.R.: Studies of interference in serial verbal reactions. J. Exp. Psychol. 18(6), 643 (1935). https://doi.org/10.1037/h0054651

  26. Jensen, A.R., Rohwer Jr., W.D.: The Stroop color-word test: a review. Acta Physiol. (Oxf) 25, 36 (1966)

    Google Scholar 

  27. Kane, M.J., Engle, R.W.: Working-memory capacity and the control of attention: the contributions of goal neglect, response competition, and task set to Stroop interference. J. Exp. Psychol. Gen. 132(1), 47 (2003)

    Article  Google Scholar 

  28. Ergen, M., Saban, S., Kirmizi-Alsan, E., Uslu, A., Keskin-Ergen, Y., Demiralp, T.: Time-frequency analysis of the event-related potentials associated with the Stroop test. Int. J. Psychophysiol. 94(3), 463 (2014). https://doi.org/10.1016/j.ijpsycho.2014.08.177

    Article  Google Scholar 

  29. Barwick, F., Arnett, P., Slobounov, S.: EEG correlates of fatigue during administration of a neuropsychological test battery. Clin. Neurophysiol. 123(2), 278 (2012). https://doi.org/10.1016/j.clinph.2011.06.027

    Article  Google Scholar 

  30. Karakas, S., Erdogan, E., Soysal, A., Ulusoy, T., Ulusoy, I., Alkan, S.: Stroop Testi TBAG Formu: Turk Kulturune Standardizasyon Calismalari. Turk Psikiyatri Derg. 2, 75 (1999)

    Google Scholar 

  31. Vigârio, R., Särelä, J., Jousmäki, V., Hämäläinen, M., Oja, E.: Independent component approach to the analysis of EEG and MEG recordings. IEEE Trans. Biomed. Eng. 47(5), 589 (2000). https://doi.org/10.1109/10.841330

    Article  Google Scholar 

  32. Lashgari, E., Liang, D., Maoz, U.: Data augmentation for deep-learning-based electroencephalography. J. Neurosci. Methods 346(July), 108885 (2020). https://doi.org/10.1016/j.jneumeth.2020.108885

    Article  Google Scholar 

  33. Sakai, A., Minoda, Y., Morikawa, K.: Data augmentation methods for machine-learning-based classification of bio-signals. In: BMEiCON 2017—10th Biomedical Engineering International Conference 2017-January, p. 1 (2017). https://doi.org/10.1109/BMEiCON.2017.8229109

  34. Welch, P.D.: The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70 (1967). https://doi.org/10.1109/TAU.1967.1161901

    Article  Google Scholar 

  35. Grabczewski, K., Jankowski, N.: Feature selection with decision tree criterion. In: Proceedings—HIS 2005: Fifth International Conference on Hybrid Intelligent Systems 2005, p. 212 (2005). https://doi.org/10.1109/ICHIS.2005.43

  36. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. CRC Press, Boca Raton (1984)

    MATH  Google Scholar 

  37. Xia, F., Zhang, W., Li, F., Yang, Y.: Ranking with decision tree. Knowl. Inf. Syst. 17(3), 381 (2008). https://doi.org/10.1007/s10115-007-0118-y

    Article  Google Scholar 

  38. Gholamiangonabadi, D., Kiselov, N., Grolinger, K.: Deep neural networks for human activity recognition with wearable sensors: leave-one-subject-out cross-validation for model selection. IEEE Access 8, 133982 (2020)

    Article  Google Scholar 

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Acknowledgements

This study was supported by the Scientific Research Projects of Kütahya Dumlupınar University within the scope of the project numbered 2020/26. The project was carried out within the scope of Kütahya Dumlupınar University Neurotechnology Education Application and Research Center (NÖTEM).

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Authors and Affiliations

  1. Department of Electrical Electronics Engineering, Faculty of Engineering, Kütahya Dumlupinar University, Kütahya, Turkey

    Evin Şahin Sadık & Hamdi Melih Saraoğlu

  2. Department of Neurology, Faculty of Medicine, Kütahya Health Sciences University, Kütahya, Turkey

    Sibel Canbaz Kabay & Gönül Akdağ

  3. Department of Electrical Electronics Engineering, Simav Faculty of Technology, Kütahya Dumlupinar University, Kütahya, Turkey

    Mustafa Tosun

  4. Department of Neuropsychology, Bakirkoy Training and Research Hospital for Psychiatry Neurology and Neurosurgery, Istanbul, Turkey

    Cahit Keskinkılıç

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  1. Evin Şahin Sadık
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  2. Hamdi Melih Saraoğlu
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Şahin Sadık, E., Saraoğlu, H.M., Canbaz Kabay, S. et al. Investigation of the effect of rosemary odor on mental workload using EEG: an artificial intelligence approach. SIViP 16, 497–504 (2022). https://doi.org/10.1007/s11760-021-01992-5

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  • DOI: https://doi.org/10.1007/s11760-021-01992-5

Keywords

  • EEG
  • Mental workload
  • Stroop test
  • CART
  • Rosemary odor
  • Classification