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dc.contributor.authorDozorska, Oksana
dc.contributor.authorDozorskyi, Vasil
dc.contributor.authorYavorska, Evhenia
dc.contributor.authorKapatsila, Yuriy
dc.contributor.authorPankiv, Iryna
dc.contributor.authorKubashok, Andriy
dc.coverage.temporal15-17 December 2021
dc.date.accessioned2021-12-28T20:03:15Z-
dc.date.available2021-12-28T20:03:15Z-
dc.date.created2021-12-15
dc.date.issued2021-12-15
dc.identifier.citationThe methods of biosignals processing and their implementation in the structure of the system of impaired human communicative function compensation / Oksana Dozorska, Vasil Dozorskyi, Evhenia Yavorska, Yuriy Kapatsila, Iryna Pankiv, Andriy Kubashok // ICAAEIT 2021, 15-17 December 2021. — Tern. : TNTU, Zhytomyr «Publishing house „Book-Druk“» LLC, 2021. — P. 151–156. — (Biomedical engineering).
dc.identifier.isbn978-617-8079-60-4
dc.identifier.urihttp://elartu.tntu.edu.ua/handle/lib/36941-
dc.description.abstractThe methods of biosignals processing and structure of the system of impaired human communicative function compensation are proposed. The methods of biosignals processing are based on the application of spectral-correlation analysis methods using the sliding window method and include preparatory and main stages. The sequence of steps of biosignals processing at the preparatory and main stages is substantiated. Also technical parameters and features of practical realization of constituent elements of the system of impaired human communicative function compensation are offered. The principle of operation of the system is based on the parallel selection and processing of electroencephalographic and electromyographic signals. At the same time the features of electrode constructions for selection of electroencephalographic signals, which are widespread today, as well as possible artifacts that will arise in the process of selection of these signals are analyzed. The design of electrodes for registration of electroencephalographic and electromyographic signals is proposed, the selection and processing of which is the basis of the method of indirect compensation of the impaired communicative function, which is realized by this system. A variant of realization of the functional diagram of the block of biosignals selection is proposed. The proposed design of the system of impaired human communicative function compensation can be manufactured using 3D printing technology, which will reduce its cost.
dc.format.extent151-156
dc.language.isoen
dc.publisherTNTU, Zhytomyr «Publishing house „Book-Druk“» LLC
dc.relation.ispartofProceedings of the International Conference „Advanced applied energy and information technologies 2021”, 2021
dc.subjecthuman communicative function
dc.subjectcompensation system
dc.subjectprocessing methods
dc.subjectspectral-correlation analysis
dc.subjectelectrodes
dc.subjectblock of biosignals selection
dc.titleThe methods of biosignals processing and their implementation in the structure of the system of impaired human communicative function compensation
dc.typeConference Abstract
dc.rights.holder© Ternopil Ivan Puluj National Technical University, Ukraine, 2021
dc.coverage.placenameTernopil
dc.format.pages6
dc.relation.referencesen1. Brigham, K., Vijaya Kumar B.V.K. (2010). Imagined Speech Classification with EEG Signals for Silent Communication: A Preliminary Investigation into Synthetic Telepathy. Bioinformatics and Biomedical Engineering, Proceedings of the 4th International Conference. Chengdu, China.
dc.relation.referencesen2. Porbadnigk, A., Wester, M., Schultz, T. (2009). EEG-Based Speech Recognition: Impact of Temporal Effects. Bio-inspired Systems and Signal, Processing, Proceedings of the 2nd International Conference. Porto. Portugal.
dc.relation.referencesen3. Meltzner, G.S., Heaton, J.T., Deng, Y., De Luca, G., Roy, S.H., Joshua, C. Kline. (2017). Silent Speech Recognition as an Alternative Communication Device for Persons with Laryngectomy. IEEE/ACM Trans Audio Speech Lang Process, Proceedings of the Conference.
dc.relation.referencesen4. Herff, C., Schultz, T. (2016). Automatic speech recognition from neural signals: A focused review. Frontiers in Neuroscience, 10, 1-7.
dc.relation.referencesen5. Wand, M., Schmidhuber, J. (2016). Deep Neural Network Frontend for Continuous EMG-based Speech Recognition. International Speech Communication Association (Interspeech), Proceedings of the 17th Annual Conference. San Francisco, USA.
dc.relation.referencesen6. Jorgensen, C., Lee, D.D., Agabon, S. (2003). Sub Auditory Speech Recognition Based on EMG/EPG Signals. Neural Networks, Proceedings of the International Joint Conference.
dc.relation.referencesen7. Khan, M., Jahan, M. (2016). Sub-vocal speech pattern recognition of Hindi alphabet with surface electromyography signal. Perspectives in Science, 8, 558-560.
dc.relation.referencesen8. Gonzalez, J.A., Cheah, L.A., Gilbert, J.M., Bai, J., Ell, S.R., Green, P.D., Moore, R.K. (2016). A silent speech system based on permanent magnet articulography and direct synthesis. Computer Speech & Language, 67–87.
dc.relation.referencesen9. Xueqian, J., Li, J., Du, Y. (2008). Unvoiced Speech Recognition Based on One-Channel Facial Myoelectric Signal. Intelligent Control and Automation, Proceedings of the Sixth World Congress. Dalian, China.
dc.relation.referencesen10. Sannykov, V. (2008). Syloj mysli. Populiarnaia mekhanika, 6(68), 72-75. (in Russian).
dc.relation.referencesen11. Dozorska, O. (2018). The mathematical model of electroenсephalographic and electromyographic signals for the task of human communicative function restoration. Scientific jornal of the Ternopil National Technical University, 92(4), 126–132.
dc.relation.referencesen12. Nykytyuk, V., Dozorskyi, V., Dozorska, O. (2018). Detection of biomedical signals disruption using a sliding window. Scientific jornal of the Ternopil National Technical University, 91(3), 125–133.
dc.identifier.citationenDozorska O., Dozorskyi V., Yavorska E., Kapatsila Y., Pankiv I., Kubashok A. (2021) The methods of biosignals processing and their implementation in the structure of the system of impaired human communicative function compensation. ICAAEIT 2021 (Tern., 15-17 December 2021), pp. 151-156.
dc.contributor.affiliationTernopil Ivan Puluj National Technical University, Rus’ka str. 56, 46001, Ternopil, Ukraine
dc.citation.spage151
dc.citation.epage156
Розташовується у зібраннях:International conference „Advanced Applied Energy and Information Technologies 2021“, (ICAAEIT 2021)



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