Abstract
Detection and classification of motor-related brain patterns from non-invasive electroencephalograms (EEGs) is challenging due to their non-stationarity and low signal-to-noise ratio and requires using advanced mathematical approaches. Traditionally applied methods such as time-frequency analysis and spatial filtering allow to quantify the main attribute of the motor-related brain activity – contralateral desynchronization of mu-band oscillations (8-13 Hz) in sensorimotor cortex – by measuring EEG signal’s amplitude, power spectral density, location etc. However, these features suffer from strong inter- and intra-subject variability. So, special attention is paid to the finding of stable features. In present paper, we investigate application of the recurrence plots – robust mathematical tool for nonstationary data analysis – to explore properties of motor-related EEG samples. Our goal is to show that recurrence plots are sensitive to the changes in brain activity accessed from noninvasive EEG recordings and may provide us a new context for interpretation of motor-related pattern in EEG.
Highlights
Detecting motor-related events using EEG signal is highly demanded in the area of brain-computer interfaces development for post-stroke rehabilitation and control of external devices
The progress made in this area allowed to connect motor action with certain phenomena occurring in bran activity, forming the idea of how motor task execution is reflected in human brain
We show that measures of recurrence quantification analysis (RQA) are sensitive to transitions from random uncorrelated background EEG to motor task accomplishment, allowing to detect movement onset
Summary
Detecting motor-related events using EEG signal is highly demanded in the area of brain-computer interfaces development for post-stroke rehabilitation and control of external devices. ERD detection is done via time-frequency analysis [Wang et al, 2004; Ince et al, 2007; Maksimenko et al, 2018b] with the decrease of spectral power density as a classification criteria [Carrera-Leon et al, 2012; Xu and Song, 2008]. There are inherent limitations of the mentioned approaches, such as the lack of inter- and intrasubjects robustness and the computational demands. For this reason, we introduce the approach which uses concept of recurrences to characterize complexity of the process via its signal analysis. RPs were applied to biological data analysis [Hirata et al, 2016; Acharya et al, 2011; Acharya et al, 2013], seismic activity analysis [Lin et al, 2015; Chelidze and Matcharashvili, 2015], chemistry [Alves et al, 2017; Facchini et al, 2009], and were especially demanded in climate research [Deng et al, 2017; Feng and Dijkstra, 2017; Garcıa-Olivares and Herrero, 2013; Panagoulia and Vlahogianni, 2018]
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