Research on physiological signal analysis based on clinical statistics and machine learning

Jiaqi Feng

doi:10.1117/12.2669358

24 March 2023 Research on physiological signal analysis based on clinical statistics and machine learning

Jiaqi Feng

Proceedings Volume 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022); 126112G (2023) https://doi.org/10.1117/12.2669358
Event: International Conference on Biological Engineering and Medical Science (ICBioMed2022), 2022, Oxford, United Kingdom

Abstract

Neural network evaluation and decoding technology based on physiological signal analysis has become a hot research topic in the field of life and health. The decoding of physiological signals can enable people to break through physical limitations to communicate with the external environment, and can also enhance the effectiveness of the human body by evaluating people's cognitive and motor functions. Electroencephalogram (EEG), as a kind of physiological signal, has become a hot spot of much research due to its non-invasive, high time precision and strong explanatory characteristics. This paper summarizes the applied research based on physiological signal analysis such as EEG from three aspects of evaluation technology, human-computer interaction and clinical application, and looks forward to the development direction in the future.

Citation Download Citation

Jiaqi Feng "Research on physiological signal analysis based on clinical statistics and machine learning", Proc. SPIE 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022), 126112G (24 March 2023); https://doi.org/10.1117/12.2669358

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KEYWORDS

Electroencephalography

Brain

Signal analysis

Machine learning

Electromyography

Biomedical applications

Brain-machine interfaces

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