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Classification of pleasant and unpleasant odor imagery EEG signals

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Abstract

Electroencephalography (EEG) is a widely used technique that allows researchers to measure neural activity that demonstrates how the human brain reacts to various environmental stimuli or imaginations. In this study, EEG was used to determine the brain’s reactions during the imagination of the most pleasant and unpleasant odors among four kinds of odors, including orange, clove, thyme, and mint. The distinguishability of brain responses to these odors was tested using the Hilbert transform, Fast Walsh–Hadamard transform, band power, and spectrogram image features. The results showed that the Hilbert Transform-based features have great potential to classify the EEG signals recorded during the imagination of the most pleasant and unpleasant odors. The proposed method achieved an average classification accuracy of 87.75% for the test data with a k-nearest neighbor classifier.

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Data have been previously presented and can be provided after the request from the corresponding author.

References

  1. Dzau V, Balatbat C (2018) Health and societal implications of medical and technological advances. Science Trans Mede 10(463):eaau4778. https://doi.org/10.1016/j.mayocp.2020.07.040

  2. Weber Y, Biskup S, Helbig K, Von Spiczak S, Lerche H (2017) The role of genetic testing in epilepsy diagnosis and management. Expert Rev Mol Diagn 17(8):739–750. https://doi.org/10.1080/14737159.2017.1335598

    Article  Google Scholar 

  3. Moreira F, Sale M, Di Lorenzo M (2017) Towards timely Alzheimer diagnosis: a self-powered amperometric biosensor for the neurotransmitter acetylcholine. Biosens Bioelectron 87:607–614. https://doi.org/10.1016/j.bios.2016.08.104

    Article  Google Scholar 

  4. Dauwels J, Vialatte FB, Cichocki A (2011) On the early diagnosis of Alzheimer’s disease from EEG signals: a mini-review. Adv Cogn Neurodyn II:709–716. https://doi.org/10.1007/978-90-481-9695-1_106

    Article  Google Scholar 

  5. De Oliveira A, De Santana M, Andrade M, Gomes J, Rodrigues M, dos Santos W (2020) Early diagnosis of Parkinson’s disease using EEG, machine learning and partial directed coherence. Res Biomed Eng 36(3):311–331. https://doi.org/10.1007/s42600-020-00072-w

    Article  Google Scholar 

  6. Sharma M, Acharya U (2021) Automated detection of schizophrenia using optimal wavelet-based l1 norm features extracted from single-channel EEG. Cogn Neurodyn 15(4):661–674. https://doi.org/10.1007/s11571-020-09655-w

    Article  Google Scholar 

  7. Chan W, Levsen M, McCrae C (2018) A meta-analysis of associations between obesity and insomnia diagnosis and symptoms. Sleep Med Rev 40:170–182. https://doi.org/10.1016/j.smrv.2017.12.004

    Article  Google Scholar 

  8. Corsi MC, Chavez M, Schwartz D, Hugueville L, Khambhati A, Bassett D, De Vico Fallani F (2019) Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interface. Int J Neural Syst 29(01). https://doi.org/10.1142/S0129065718500144

  9. Zhang Y, Guo D, Li F, Yin E et al (2018) Correlated component analysis for enhancing the performance of SSVEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 26(5):948–956. https://doi.org/10.1109/tnsre.2018.2826541

    Article  Google Scholar 

  10. Aydemir O (2017) Olfactory recognition based on EEG gamma-band activity. Neural Comput 29(6):1667–1680. https://doi.org/10.1162/NECO_a_00966

    Article  MathSciNet  MATH  Google Scholar 

  11. Kroupi E, Yazdani A, Vesin JM, Ebrahimi T (2014) EEG correlates of pleasant and unpleasant odor perception. ACM Tran Multimedia Comput Commun Appl (TOMM) 11(1):1–17. https://doi.org/10.1145/2637287

    Article  Google Scholar 

  12. Yazdani A, Kroupi E, Vesin JM, Ebrahimi T (2012) Electroencephalogram alterations during perception of pleasant and unpleasant odors. IEEE, Melbourne. https://doi.org/10.1109/qomex.2012.6263860

  13. Pistoia F, Carolei A, Iacoviello D et al (2015) EEG-detected olfactory imagery to reveal covert consciousness in minimally conscious state. Brain Inj 29(13–14):1729–1735. https://doi.org/10.3109/02699052.2015.1075251

    Article  Google Scholar 

  14. Djordjevic J, Zatorre R, Petrides M, Boyle J, Jones Gotman M (2005) Functional neuroimaging of odor imagery. Neuroimage 24(3):791–801. https://doi.org/10.1016/j.neuroimage.2004.09.035

    Article  Google Scholar 

  15. Aydemir O, Naser A, Ozturk M, (2020) Classification of EEG signals recorded while imagining odors with effective band pass filter parameters. In: 2020 28th signal processing and communications applications conference (SIU) Gaziantep. https://doi.org/10.1109/siu49456.2020.9302244

  16. Sirinukunwattana K, Raza S, Tsang YW, Snead D, Cree I, Rajpoot N (2016) Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imag 35(5):1196–1206. https://doi.org/10.1109/tmi.2016.2525803

    Article  Google Scholar 

  17. Wu H, Prasad S (2017) Semi-supervised deep learning using pseudo labels for hyperspectral image classification. IEEE Trans Image Process 27(3):1259–1270. https://doi.org/10.1109/tip.2017.2772836

    Article  MathSciNet  MATH  Google Scholar 

  18. Freeman W (2007) Hilbert transform for brain waves. Scholarpedia 2(1):1338. https://doi.org/10.4249/scholarpedia.1338

    Article  Google Scholar 

  19. Freeman W, Burke B, Holmes M (2003) Aperiodic phase re-setting in scalp EEG of beta-gamma oscillations by state transitions at alpha-theta rates. Hum Brain Mapp 19(4):248–272. https://doi.org/10.1002/hbm.10120

    Article  Google Scholar 

  20. Saka K, Aydemir O, Ozturk M (2016) Classification of EEG signals recorded during right/left hand movement imagery using Fast Walsh Hadamard Transform based features.In: 2016 39th international conference on telecommunications and signal processing (TSP). Vienna. https://doi.org/10.1109/tsp.2016.7760909

  21. Saraiva A, Castro F, Nascimento R, Melo R et al (2020) Electroencephalography applied compression algorithms qualitative analysis. Comput Methods Biomech Biomed Eng Imaging Visual 8(4):367–373. https://doi.org/10.1080/21681163.2019.1673212

    Article  Google Scholar 

  22. Aydemir O (2020) Detection of highly motivated time segments in brain computer interface signals. IETE J Res 66(1):3–13. https://doi.org/10.1080/03772063.2018.1476190

    Article  MathSciNet  Google Scholar 

  23. Junwei L, Ramkumar S, Emayavaramban G, Vinod D et al (2018) Brain computer interface for neurodegenerative person using electroencephalogram. IEEE Access 7:2439–2452. https://doi.org/10.1109/access.2018.2886708

    Article  Google Scholar 

  24. Yuan L, Cao J (2017) Patients’ EEG data analysis via spectrogram image with a convolution neural network. In: International conference on intelligent decision technologies. https://doi.org/10.1007/978-3-319-59421-7_2

  25. Ramos Aguilar R, Olvera Lopez J, OlmosPineda I, Sanchez Urrieta S (2020) Feature extraction from EEG spectrograms for epileptic seizure detection. Pattern Recogn Lett 133:202–209. https://doi.org/10.1016/j.patrec.2020.03.006

    Article  Google Scholar 

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Acknowledgements

This study was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) with project number 118E241. In addition, the authors thank the participants who participated in the experiment.

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Authors and Affiliations

  1. Faculty of Engineering, Department of Electrical & Electronics Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

    Amir Naser & Onder Aydemir

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  1. Amir Naser
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  2. Onder Aydemir
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Correspondence to Amir Naser.

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Naser, A., Aydemir, O. Classification of pleasant and unpleasant odor imagery EEG signals. Neural Comput & Applic 35, 9105–9114 (2023). https://doi.org/10.1007/s00521-022-08171-8

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  • DOI: https://doi.org/10.1007/s00521-022-08171-8

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