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The corticomuscular coupling (CMC) characterization between the motor cortex and muscles during motion control is a valid biomarker of motor system function after stroke, which can improve clinical decision-making. However, traditional CMC analysis is mainly based on the coherence method that can’t determine the coupling direction, whereas Granger Causality (GC) is limited in identifying linear cause–effect relationship. In this paper, a time-frequency domain copula-based GC (copula-GC) method is proposed to assess CMC characteristic. The 32-channel electroencephalogram (EEG) signals over brain scalp and electromyography (EMG) signals from upper limb were recorded during controlling and maintaining steady-state force output for five stroke patients and five healthy controls. Then, the time-frequency copula-GC analysis was applied to evaluate the CMC strength in both directions. Experimental results show that the CMC strength of descending direction is greater than that of ascending direction in the time domain for healthy controls. With the increase of grip strength, the bi-directional CMC strength has an increasing trend. Meanwhile, the bi-directional CMC strength of right hand is larger than that of left hand. In addition, the bi-directional CMC strength of stroke patients is lower than that of healthy controls. In the frequency domain, the strongest CMC is observed at the beta frequency band. Additionally, the CMC strength of descending direction is slightly larger than that of ascending direction in healthy controls, while the CMC strength of descending direction is lower than that of ascending direction in stroke patients. We suggest that the proposed time-frequency domain analysis approach based on copula-GC can effectively detect complex functional coupling between cortical oscillations and muscle activities, and provide a potential quantitative analysis measure for motion control and rehabilitation evaluation.

This study explores the effect of disaggregated oil price shocks on Zambia’s historic headline inflation rates. To quantify the contemporaneous impact of oil price shocks on inflation, a Structural Vector Autoregressive Model (SVAR) is utilised, which is supplemented with Impulse Response Functions (IRFs), Granger Causality Tests and Forecast Error Variance Decomposition (FEVD). After satisfying the cointegration criteria, long-run relationships are examined using the Vector Error Correction Model (VECM).The findings show that decomposed oil price shocks do not have a short-run contemporaneous impact on inflation at the 5% level and that oil price shocks do not Granger-cause inflation. The insignificance of the short-run inflationary pass-through effect is attributed to Zambia’s historic price controls, fuel subsidies, exchange rate controls and increased credibility of monetary policy. Notably, of the three types of oil price shocks, FEVD results show that global aggregate demand shocks are attributed for the largest variation in Zambia’s inflation rates, i.e. 1.8%. Long-run analysis using VECM shows that global aggregate demand and oil supply shocks both have a significantly negative long-run impact on inflation, while oil-specific demand shocks have a significantly positive impact on inflation. The speed of adjustment of inflation back to equilibrium after a short-run deviation is shown to be significant and monotonic.