|
Abstract.
The article offers a description of the developed software service designed to automate the process of preprocessing and filtering EEG signal data with synchronized video recording for the analysis of continuous brain processes. The signal is presented in the form of a matrix of topographic maps оf the signal power distribution in the specified frequency ranges, which allows a data processing specialist to perform a comparative analysis of several EEG recordings. The service provides an opportunity for a detailed analysis of the selected recording fragment, including an assessment of the dynamics of changes in the parameters of the recording fragment over time. The service allows to perform such an analysis using a synchronized video recording of a participant with video tracking of his physiological parameters, such as respiratory rate, blood pressure, pulse, oxygen saturation of blood, head movements, open/closed mouth and eyes. This analytics provides a flexible system for filtering and preprocessing EEG data. The approbation of the service for processing and analyzing EEG data was carried out on the example of automating the method of recognizing a person's meditative state, characterized by directing attention to body sensations and abstracting from external stimuli.
Keywords:
EEG signal, spatial power distribution, classification of brain activity states.
PP. 72-85.
DOI 10.14357/20718632230407
EDN HUUWNP References
1. Cox R., Fell J. Analyzing human sleep EEG: A methodological primer with code implementation. Sleep Medicine Reviews. 2020; 54 (12):101353. 2. Al-Salman W., Li Y., Oudah A., Almaged S. Sleep stage classification in EEG signals using the clustering approach based probability distribution features coupled with classification algorithms. Neuroscience Research. 2023; 188 (3): 51-67. 3. Cahn R., Polich J. Meditation states and traits: EEG, ERP, and neuroimaging studies. Psychological Bulletin. 2006; 4. Díaz M. H., Córdova F. M., Cañete L., Palominos F., Cifuentes F., Rivas G. Inter-channel Correlation in the EEG Activity During a Cognitive Problem Solving Task with an Increasing Difficulty Questions Progression. Procedia Computer Science. 2015; 55: 1420-1425. 5. Díaz M. H., Córdova F. M., Cañete L., Palominos F., Cifuentes F., Sánchez C., Herrera M. Order and Chaos in the Brain: Fractal Time Series Analysis of the EEG Activity During a Cognitive Problem Solving Task. Procedia Computer Science. 2015; 55: 1410-1419. 6. Tong S., Thakor N. V. Quantitative EEG Analysis Methods and Clinical Applications. Artech House. 2009; 7. Erlichman M., Electroencephalographic (EEG) video monitoring. Health Technol Assess Rep. 1990;(4):1-14. PMID: 2104066. 8. Mizrahi E. M. Electroencephalographic-video monitoring in neonates, infants, and children. J Child Neurol. 1994; 9 (l): 46-56. 9. Mizrahi E. M. Pediatric electroencephalographic video monitoring. J Clin Neurophysiol. 1999; 16(2):100-110. 10. Othman W., Kashevnik A., Ryabchikov I., Shilov N. Contactless Camera-Based Approach for Driver Respiratory Rate Evaluation in Vehicle Cabin. 2022 Intelligent Systems Conference, Amsterdam, The Netherlands, 1-2, September 2022, Springer, Cham. 2022. Vol. 2. 11. Othman W., Kashevnik A. Video-Based Real-Time Heart Rate Detection for Drivers Inside the Cabin Using a Smartphone. 2022 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS 2022), Bali, Indonesia, 24-26 November 2022IEEE. 2022. 1–5. 12. Hamoud B., Kashevnik A., Othman W., Shilov N. Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation. Sensors, MDPI AG, Basel, Switzerland. 2023. Vol. 23(4). P. 1–16. 13. Das R. K., Martin A., Zurales T., Dowling D., Khan A. A Survey on EEG Data Analysis Software. Sci. 2023; 5(2): 23. 14. Ahani A., Wahbeh H., Nezamfar H. Quantitative change of EEG and respiration signals during mindfulness meditation. J NeuroEngineering Rehabil. 2014; 11: 87. 15. Padmavathi K., Meenakshi K., Swaraja K., Rajani A., Raju M. S. EEG based interpretation of human brain activity during yoga and meditation using machine learning: A systematic review. Complementary Therapies in Clinical Practice. 2021; 43: 101329. 16. Cahn B. R., Delorme A. Polich J. Occipital gamma activation during Vipassana meditation. Cogn Process. 2010; 11: 39–56. 17. Travis F. Comparison of coherence, amplitude, and eLORETA patterns during Transcendental Meditation and TM-Sidhi practice. International J. of Psychophysiology. 2011; 81(3): 198-202. 18. Kjaer T. W., Bertelsen C., Piccini P., Brooks D, Alving J, Lou H. C. Increased dopamine tone during meditation-induced change of consciousness. Cogn. Brain Research. 2002; 13(2): 255-259. 19. Oppenheim A. V., Schafer R., Buck J. R. Discrete-Time Signal Processing. Prentice Hall. 1999. 20. Deng J., Guo J., Yang J., Xue N., Kotsia I., Zafeiriou S. ArcFace: Additive Angular Margin Loss for Deep Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 10, pp. 5962-5979, 1 Oct. 2022. 21. Yaqoob M. K., Ali S. F., Bilal M., Hanif M. S., Al-Saggaf U. M. ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection. Sensors, 20023; 21(11): 3883.
|
