Guo, Zhenghao

English

This thesis aims to develop signal processing and information-theoretic methodologies for the detection and characterization of cortico-muscular in- teractions (CMI) using the electroencephalogram (EEG) recorded over the sensorimotor cortex and the electromyogram (EMG) of active muscles. Com- putational methods most commonly used in this context are cortico-muscular coherence (CMC) and Granger causality (GC) analysis, which are typically not suﬃciently sensitive: some healthy subjects exhibit no signiﬁcant CMC and GC, and yet have good motor skills. To enhance synchronous cortico- muscular components in mixtures captured by EEG and EMG, a concept of coherent subband independent component analysis (CoSICA) is introduced.The methodology is accomplished through ﬁlter bank processing to decompose EEG and EMG signals into frequency bands, followed by independent com- ponent analysis, a novel component selection algorithm, and a re-synthesis of EEG and EMG to improve CMC levels. Results from simulations and neu- rophysiological signals demonstrate that CoSICA signiﬁcantly enhances origi- nal CMC levels. Additionally, a multiscale wavelet transfer entropy (MWTE) methodology is proposed to develop measures of functional CMI with improved sensitivity and can detect both linear and non-linear couplings. This method- ology uses a dyadic stationary wavelet transform to decompose EEG and EMG signals into functional bands of neural oscillations and applies transfer entropy analysis with a range of embedding delay vectors to detect and quantify intra- and cross-frequency couplings at diﬀerent time scales. Experiments with simu- lated and neurophysiological signals validate the potential of MWTE to detect and quantify information ﬂows, including non-linear cross-frequency interac- tions and interactions across diﬀerent temporal scales. Finally, the inferred MWTE method is applied to data collected from young people with dystonia, revealing a distinct impairment of cross-frequency feedback from muscle to brain in dystonia, indicating potential for further exploration in this ﬁeld

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Signal Processing and Information-Theoretic Methods for Detection and Categorisation of Cortico-Muscular Interactions

https://kclpure.kcl.ac.uk/portal/files/222462657/2023_Guo_Zhenghao_1814867_ethesis.pdf

Signal Processing and Information-Theoretic Methods for Detection and Categorisation of Cortico-Muscular Interactions

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