Olfactory electroencephalogram signal recognition based on wavelet energy moment_Journal of Biomedical Engineering

Authors：

ZHAI Wenpeng , ZHANG Xiaonei , HOU Huirang ,  MENG Qinghao

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, P.R.China;

Corresponding?author：

MENG Qinghao, Email: qh_meng@tju.edu.cn

Keywords：

olfactory; electroencephalogram; wavelet energy moment; pattern recognition

DOI：

10.7507/1001-5515.201910036

Video：

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Studying the ability of the brain to recognize different odors is of great significance in the assessment and diagnosis of olfactory dysfunction. The wavelet energy moment (WEM) was proposed as a feature of olfactory electroencephalogram (EEG) signal and used for odor classification. Firstly, the olfactory evoked EEG data of 13 odors were collected by an experiment. Secondly, the WEM was extracted from olfactory evoked EEG data as the signal feature, and the power spectrum density (PSD), approximate entropy, sample entropy and wavelet entropy were used as the contrast features. Finally, k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF) and decision tree classifier were used to identify different odors. The results showed that using the above four classifiers, the classification accuracy of WEM feature was higher than other features, and the k-NN classifier combined with WEM feature had the highest classification accuracy (91.07%). This paper further explored the characteristics of different EEG frequency bands, and found that most of the classification accuracy based on the features of γ band was better than that of the full band and other bands, among which the WEM feature of the γ band combined with the k-NN classifier had the highest classification accuracy (93.89 %). The research results of this paper could provide a new objective basis for the evaluation of olfactory function. On the other hand, it could also provide new ideas for the study of olfactory-induced emotions.

Citation： ZHAI Wenpeng, ZHANG Xiaonei, HOU Huirang, MENG Qinghao. Olfactory electroencephalogram signal recognition based on wavelet energy moment. Journal of Biomedical Engineering, 2020, 37(3): 399-404. doi: 10.7507/1001-5515.201910036 Copy

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18.	Aydemir ?. Olfactory recognition based on EEG Gamma-band Activity. Neural Comput, 2017, 29(6): 1667-1680.
19.	邵晨曦, 盧繼軍, 周顥. 基于小波變換的腦電圖癲癇波形檢測. 生物醫學工程學雜志, 2002, 19(2): 259-263.
20.	徐寶國, 宋愛國, 王愛民. 基于小波包能量的腦電信號特征提取方法. 東南大學學報:自然科學版, 2010, 40(6): 1203-1206.
21.	周衛東, 尹立新, 王照軍. 腦電在線分析系統的研究及小波變換的應用. 生物醫學工程學雜志, 2001, 18(2): 169-172.
22.	Yang H, Yu L. Feature extraction of wood-hole defects using wavelet-based ultrasonic testing. Journal of Forestry Research, 2017, 28(2): 395-402.
23.	Namazi H, Akrami A, Nazeri S, et al. Analysis of the influence of complexity and entropy of odorant on fractal dynamics and entropy of EEG signal, BioMed Research International, 2016: 1-5.
24.	Pincus S M. Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 1991, 88(6): 2297-2301.
25.	Richman J S, Moorman J R. Physiological time-series analysis, using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049.
26.	李昕, 蔡二娟, 田彥秀, 等. 一種改進腦電特征提取算法及其在情感識別中的應用. 生物醫學工程學雜志, 2017, 34(4): 510-517, 528.

1. 廖凱. 嗅覺刺激對學習和記憶的影響及其機制的研究. 武漢: 華中科技大學, 2011.
2. Ehrlichman H, Bastone L. Olfaction and emotion//Serby M J, Chobor K L. Science of olfaction. New York: Springer, 1992: 410-438.
3. Skori? M K, Adamec I, Jerbi? A B, et al. Electroencephalographic response to different odors in healthy individuals: a promising tool for objective assessment of olfactory disorders. Clinical EEG and Neuroscience, 2014, 46(4): 370-376.
4. Chen M C, Fang S H, Fang L. The effects of aromatherapy in relieving symptoms related to job stress among nurses. International Journal of Nursing Practice, 2015, 21(1): 87-93.
5. Nie D, Wang X W, Shi L C, et al. EEG-based emotion recognition during watching movies//International IEEE/EMBS Conference on Neural Engineering, Cancun: IEEE, 2011: 667-670.
6. Jatupaiboon N, Pan-Ngum S, Israsena P. Real-time EEG-based happiness detection system, The Scientific World Journal, 2013: 618-649.
7. Siuly S, Li Y. Discriminating the brain activities for brain–computer interface applications through the optimal allocation-based approach. Neural Computing and Applications, 2015, 26(4): 799-811.
8. Billot P E, Andrieu P, Biondi A, et al. Cerebral bases of emotion regulation toward odours: a first approach. Behavioural Brain Research, 2017, 317: 37-45.
9. Millot J L, Laurent L, Casini L. The influence of odors on time perception. Frontiers in Psychology, 2016, 7: 181.
10. Whitcroft K L, Aziz M, Croy I, et al. Short inter-stimulus intervals can be used for olfactory electroencephalography in patients of varying olfactory function. Neuroscience, 2017, 363: 26-33.
11. Joussain P, Thevenet M, Rouby C, et al. Effect of aging on hedonic appreciation of pleasant and unpleasant odors. PLoS One, 2013, 8(4): e61376.
12. Frasnelli J, Christiane W, Thomas H. The influence of stimulus duration on odor perception. International Journal of Psychophysiology, 2006, 62(1): 24-29.
13. Cherninskii A A, Zima I G, Makarchouk N Y, et al. Modifications of EEG related to directed perception and analysis of olfactory information in humans. Neurophysiology, 2009, 41(1): 63-70.
14. Iacoviello D, Petracca A, Spezialetti M, et al. A classification algorithm for electroencephalography signals by self-Induced emotional stimuli. IEEE Trans Cybern, 2015, 46(12): 3171-3180.
15. Kroupi E, Vesin J M, Ebrahimi T. Subject-independent odor pleasantness classification using brain and peripheral signals. IEEE Transactions on Affective Computing, 2016, 7(4): 422-434.
16. Yavuz E, Aydemir O. Olfaction recognition by EEG analysis using wavelet transform features//International Symposium on Innovations in Intelligent Systems and Applications (INISTA), Sinaia: IEEE, 2016: 1-4.
17. Aydemir ?. Classification of electroencephalography signals recorded during smelling of valerian and rosewater odors//International Conference on Telecommunications and Signal Processing (TSP), Sinaia: IEEE, 2016: 392-395.
18. Aydemir ?. Olfactory recognition based on EEG Gamma-band Activity. Neural Comput, 2017, 29(6): 1667-1680.
19. 邵晨曦, 盧繼軍, 周顥. 基于小波變換的腦電圖癲癇波形檢測. 生物醫學工程學雜志, 2002, 19(2): 259-263.
20. 徐寶國, 宋愛國, 王愛民. 基于小波包能量的腦電信號特征提取方法. 東南大學學報:自然科學版, 2010, 40(6): 1203-1206.
21. 周衛東, 尹立新, 王照軍. 腦電在線分析系統的研究及小波變換的應用. 生物醫學工程學雜志, 2001, 18(2): 169-172.
22. Yang H, Yu L. Feature extraction of wood-hole defects using wavelet-based ultrasonic testing. Journal of Forestry Research, 2017, 28(2): 395-402.
23. Namazi H, Akrami A, Nazeri S, et al. Analysis of the influence of complexity and entropy of odorant on fractal dynamics and entropy of EEG signal, BioMed Research International, 2016: 1-5.
24. Pincus S M. Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 1991, 88(6): 2297-2301.
25. Richman J S, Moorman J R. Physiological time-series analysis, using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049.
26. 李昕, 蔡二娟, 田彥秀, 等. 一種改進腦電特征提取算法及其在情感識別中的應用. 生物醫學工程學雜志, 2017, 34(4): 510-517, 528.

Journal of Biomedical Engineering

Olfactory electroencephalogram signal recognition based on wavelet energy moment

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