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Медицинский вестник
Северного Кавказа
Научно-практический журнал
Зарегистрирован в Федеральной службе
по надзору за соблюдением законодательства
в сфере массовых коммуникаций
и охране культурного наследия
ПИ №ФС77-26521 от 7 декабря 2006 года
ISSN 2073-8137
rus
русский
eng
english

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Адрес редакции
355017, Ставрополь, улица Мира, 310.

Телефоны
(8652) 35-25-11, 35-32-29.

E-mail
medvestnik@stgmu.ru

Рейтинг@Mail.ru

Гибридная искусственная нейронная сеть CNN-MLP для распознавания людей с использованием структур частот высокого гамма-спектра ЭЭГ

[Экспериментальная медицина]
Селицкий Стас ;

В работе использована гибридная искусственная нейронная сеть типа сверточно-многослойного перцептрона для изучения структур из верхнего гамма-спектра электроэнцефалографии (ЭЭГ) для распознавания людей. В связи с этим был собран исходный набор данных ЭЭГ у различных людей, включавший записи во время различных физических действий и умственной деятельности. Выполнены исследования с высокими и низкими, гамма- и бетамасштабными свёрточными фильтрами. В результате работы отмечалась выборочная эффективность полос ЭЭГ, а функции сверточного многослойного персептрона с высоким гамма-масштабированием были признаны эффективными в персональном распознавании индивидуума

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Список литературы:
1. Acharya U. R., Oh S. L., Hagiwara Y., Tan J. H., Adeli H. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in biology and medicine. 2018;100:270-278. https://doi.org/10.1016/j.compbiomed.2017.09.017
2. Greco C., Matarazzo O., Cordasco G., Vinciarelli A., Callejas Z. [et al.] Discriminative power of EEG-based biomarkers in major depressive disorder: A systematic review. IEEE Access. 2021;РР:1-1. https://doi.org/10.1109/ACCESS.2021.3103047
3. Khare S. K., Bajaj V., Siuly S., Sinha G. Classification of schizophrenia pa- tients through empirical wavelet transformation using electroencephalogram signals. Modelling and Analysis of Active Biopotential Signals in Healthcare. 2020;1:1-26. https://doi.org/10.1088/978-0-7503-3279-8ch1
4. Islam M. N., Sulaiman N., Al Farid F., Uddin J., Alyami S. A. [et al.] Diagnosis of hearing deficiency using EEG based AEP signals: Cwt and improved-vgg16 pipeline. Peer J. Computer Science. 2021;7,e638. https://doi.org/10.7717/peerj-cs.638
5. Liu Q., Cai J., Fan S. Z., Abbod M. F., Shieh J. S. [et al.] Spectrum analysis of EEG signals using cnn to model patient’s consciousness level based on anesthesiologists’ experience. IEEE. 2019;7:53731-53742. https://doi.org/10.1109/ACCESS.2019.2912273
6. Alhagry S., Fahmy A. A., El-Khoribi R. A. Emotion recognition based on eeg using lstm recurrent neural network. Emotion. 2017;8(10):355-358.
7. Du X., Ma C., Zhang G., Li J., Lai Y. K. [et al.] An efficient lSTM network for emotion recognition from multichannel EEG signals. IEEE Trans. Affect. Comput. 2020. https://doi.org/10.1109/TAFFC.2020.3013711
8. Xing X., Li Z., Xu T., Shu L., Hu B. [et al.] SAE+ LSTM: A new framework for emotion recognition from multichannel EEG. Frontiers in neurorobotics. 2019;13(37):1-14. https://doi.org/10.3389/fnbot.2019.00037
9. Sheykhivand S., Mousavi Z., Rezaii T. Y., Farzamnia A. Recognizing emotions evoked by music using cnn-lstm networks on EEG signals. IEEE. 2020:139332-139345 https://doi.org/10.1088/1742-6596/2024/1/012044
10. Alarcao S. M., Fonseca M. J. Emotions recognition using EEG signals: A survey. IEEE Transactions on Affective Computing. 2017;10(3):374-393. https://doi.org/10.1109/TAFFC.2017.2714671
11. Zhang G., Davoodnia V., Sepas-Moghaddam A., Zhang Y., Etemad A. Classification of hand movements from EEG using a deep attention-based lstm network. IEEE Sensors Journal. 2019;20(6):3113-3122. https://doi.org/10.1109/JSEN.2019.2956998
12. Zhou H., Zhao X., Zhang H., Kuang S. The mechanism of a multi-branch structure for EEG-based motor imagery classification. In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE. 2019:2473-
2477. https://doi.org/10.3390/diagnostics12040995
13. Kurkin S. A., Pitsik E. N., Musatov V. Y., Runnova A. E., Hramov A. E. Artificial neural networks as a tool for recognition of movements by electroencephalograms. In: ICINCO. 2018;1:176-181. Available at: https://www.scitepress.org/papers/2018/68602/68602.pdf
14. Vega C. F., Quevedo J., Escandón E., Kiani M., Ding W. [et al.] Fuzzy temporal convolutional neural networks in p300-based brain – computer interface for smart home interaction. Applied Soft Computing. 2022;117:108359. https://doi.org/10.1016/j.asoc.2021.108359
15. Kang J. S., Park U., Gonuguntla V., Veluvolu K. C., Lee M. Human implicit intent recognition based on the phase synchrony of EEG signals. Pattern Recognition Letters. 2015;66:144-152. https://doi.org/10.1016/j.patrec.2015.06.013
16. Schetinin V., Jakaite L., Schult J. Informativeness of sleep cycle features in bayesian assessment of newborn electroencephalographic maturation. DBLP. 2011:1-6. https://doi.org/10.1109/CBMS.2011.5999111
17. Jakaite L., Schetinin V., Maple C. Bayesian assessment of newborn brain maturity from two-channel sleep electroencephalograms. Computational and Mathematical Methods in Medicine. 2012:629654. https://doi.org/10.1155/2012/629654
18. Schetinin V., Jakaite L. Classification of newborn EEG maturity with Bayesian averaging over decision trees. Expert Systems with Applications. 2012;39(10):9340-9347. https://doi.org/10.1016/j.eswa.2012.02.184
19. Selitsky S., Selitskaya N., Schult J. Machine learning approach to classification of sleep electroencephalograms from newborns at risk of brain pathologies. Medical News of North Caucasus. 2021;16(2):140-143. https://doi.org/10.14300/mnnc.2021.16031
20. Schetinin V., Jakaite L., Nyah N., Novakovic D., Krzanowski W. Feature extraction with GMDH-type neural networks for EEG-based person identification. International Journal of Neural Systems. 2018;28(06):1750064. https://doi.org/10.1142/S0129065717500642
21. Jakaite L., Schetinin V., Schult J. Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity. In: Proceedings of the 24th IEEE International Symposium on Computer-Based Medical Systems. 2011 https://doi.org/10.1109/CBMS.2011.5999109
22. La Rocca D., Campisi P., Scarano G. EEG biometrics for individual recognition in resting state with closed eyes. In: 2012 BIOSIG-Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG). IEEE. 2012:1-12.
23. Hammad M., Pławiak P., Wang K., Acharya U. R. Resnetattention model for human authentication using EEG signals. Expert Systems. 2021;38(6):e12547. https://doi.org/10.1111/exsy.12547
24. Michielli N., Acharya U. R., Molinari F. Cascaded lstm recurrent neural network for automated sleep stage classification using single-channel EEG signals. Computers in biology and medicine. 2019;106:71-81. https://doi.org/10.1016/j.compbiomed.2019.01.013
25. Lawhern V. J., Solon A. J., Waytowich N. R., Gordon S. M., Hung C. P., Lance B. J. EEGnet: a compact convolutional neural network for EEG-based brain – computer interfaces. Journal of Neural Engineering. 2018;15(5):056013. https://doi.org/10.1088/1741-2552/aace8c
26. Amin S. U., Muhammad G., Abdul W., Bencherif M., Alsulaiman M. Multi-CNN feature fusion for efficient EEG classification. In: 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). 2020:1-6. https://doi.org/10.1109/icmew46912.2020.9106021
27. Sheykhivand S., Mousavi Z., Rezaii T. Y., Farzamnia A. Recognizing emotions evoked by music using CNN-LSTM networks on EEG signals. IEEE. 2020:139332-139345. https://doi.org/10.3390/s22062346
28. Schetinin V., Schult J. Learning polynomial networks for classification of clinical electroencephalograms. Soft Computing. 2006;10(4):397-403
https://doi.org/10.1007/s00500-005-0499-3
29. Nyah N., Jakaite L., Schetinin V., Sant P., Aggoun A. Evolving polynomial neural networks for detecting abnormal patterns. 8th IEEE International Conference on Intelligent Systems. IEEE. 2016:74-80. https://doi.org/10.1109/IS.2016.7737403
30. Nguyen P., Tran D., Huang X., Sharma D. A proposed feature extraction method for EEG-based person identification. In: Proceedings on the International Conference on Artificial Intelligence. 2012.
31. Chang W., Wang H., Yan G., Liu C. An EEG based familiar and unfamiliar person identification and classification system using feature extraction and directed functional brain network. Expert Systems with Applications. 2020:113448. https://doi.org/10.3390/brainsci11111424
32. Gold I. Does 40-hz oscillation play a role in visual consciousness? Consciousness and Cognition. 1999;8(2): 186-195. Available at: https://www.sciencedirect.com/science/article/pii/S1053810099903999
33. Nguyen P., Tran D., Huang X., Sharma D. A proposed feature extraction method for EEG-based person identification. In: Proceedings on the International Conference on Artificial Intelligence (ICAI). 2012. 34. Schetinin V., Jakaite L. Classification of newborn EEG maturity with bayesian averaging over decision trees. Expert Syst. 2012;39(10):9340-9347. https://doi.org/10.1016/j.eswa.2012.02.184
35. Selitskiy S. Kolmogorov’s gate non-linearity as a step toward much smaller artificial neural networks. In: Proceedings of the 24th International Conference on Enterprise Information Systems. ICEIS. 2022:492-499. https://doi.org/10.5220/0011060700003179
36. Tolstikhin I. O., Houlsby N., Kolesnikov A., Beyer L., Zhai X. [et al.] MLP-mixer: An all-MLP architecture for vision. Advances in Neural Information Processing Systems. 2021. Available at: https://papers.nips.cc/paper/2021/file/cba0a4ee5ccd02fda0fe3f9a3e7b89fe-Paper.pdf
37. Yang H., Han J., Min K. A multi-column CNN model for emotion recognition from EEG signals. Sensors. 2019;19(21):4736. https://doi.org/10.3390/s19214736
38. Yang S., Deravi F. On the effectiveness of EEG signals as a source of biometric information. In: 2012 Third International Conference on Emerging Security Technologies. IEEE. 2012:49-52. https://doi.org/10.1109/EST.2012.8

Ключевые слова: ЭЭГ, персональное распознавание, высокий гамма-спектр, нейронные сети, многослойный персептрон, машинное обучение, искусственный интеллект


Учредители:
Ставропольская государственная медицинская академия
Государственный научно-исследовательский институт курортологии
Пятигорская государственная фармацевтическая академия