Background: Traditional studies on the neural mechanisms of tremor use coherence analysis to investigate the relationship between cortical and muscle activity, measured by electroencephalograms (EEG) and electromyograms (EMG). This methodology is limited by the need of relatively long signal recordings, and it is sensitive to EEG artifacts. Here, we analytically derive and experimentally validate a new method for automatic extraction of the tremor-related EEG component in pathological tremor patients that aims to overcome these limitations. Methods: We exploit the coupling between the tremor-related cortical activity and motor unit population firings to build a linear minimum mean square error estimator of the tremor component in EEG. We estimated the motor unit population activity by decomposing surface EMG signals into constituent motor unit spike trains, which we summed up into a cumulative spike train (CST). We used this CST to initialize our tremor-related EEG component estimate, which we optimized using a novel approach proposed here. Results: Tests on simulated signals demonstrate that our new method is robust to both noise and motor unit firing variability, and that it performs well across a wide range of spectral characteristics of the tremor. Results on 9 essential (ET) and 9 Parkinson's disease (PD) patients show a ~2-fold increase in amplitude of the coherence between the estimated EEG component and the CST, compared to the classical EEG-EMG coherence analysis. Conclusions: We have developed a novel method that allows for more precise and robust estimation of the tremor-related EEG component. This method does not require artifact removal, provides reliable results in relatively short datasets, and tracks changes in the tremor-related cortical activity over time.
Keywords: EEG decomposition; Parkinsonian tremor; essential tremor; pathological tremor; surface EMG decomposition.