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9 April 2020 Features of motor-related brain activity revealed via recurrence quantification analysis
Elena Pitsik, Nikita Frolov
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Proceedings Volume 11459, Saratov Fall Meeting 2019: Computations and Data Analysis: from Nanoscale Tools to Brain Functions; 1145904 (2020) https://doi.org/10.1117/12.2563542
Event: Saratov Fall Meeting 2019: VII International Symposium on Optics and Biophotonics, 2019, Saratov, Russian Federation
Abstract
We propose an approach for motor-related brain activity analysis based on the combination of continuous wavelet transform and recurrence quantification analysis (RQA). Detecting such patterns on EEG is a complex task due to the nonstationarity and complexity of EEG signal, which leads to high inter- and intra-subject variability of traditionally applied methods. We show that RQA measures of complexity, such as recurrence rate an laminarity, are very useful in detection of transitions from background to motor-related EEG. Moreover, RQA measures time dependence for upper limbs is contralateral, which allows us to distinguish two types of movements.
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Elena Pitsik and Nikita Frolov "Features of motor-related brain activity revealed via recurrence quantification analysis", Proc. SPIE 11459, Saratov Fall Meeting 2019: Computations and Data Analysis: from Nanoscale Tools to Brain Functions, 1145904 (9 April 2020); https://doi.org/10.1117/12.2563542
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KEYWORDS
Electroencephalography
Brain
Wavelets
Continuous wavelet transforms
Electromyography
Time-frequency analysis
Neuroscience
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