Cross-spectral analysis of physiological tremor and muscle activity. I. Theory and application to unsynchronized EMG

We investigate the relationship between the extensor electromyogram (EMG) and tremor time series in physiological hand tremor by cross-spectral analysis. Special attention is directed to the phase spectrum and the effects of observational noise. We calculate the theoretical phase spectrum for a second order linear stochastic process and compare the results to measured tremor data recorded from subjects who did not show a synchronized EMG activity in the corresponding extensor muscle. The results show that physiological tremor is well described by the proposed model and that the measured EMG represents a Newtonian force by which the muscle acts on the hand.Comment: 9 pages, 6 figures, to appear in Biological Cybernetic

Estimation of time delay by coherence analysis

Using coherence analysis (which is an extensively used method to study the correlations in frequency domain, between two simultaneously measured signals) we estimate the time delay between two signals. This method is suitable for time delay estimation of narrow band coherence signals for which the conventional methods cannot be reliably applied. We show by analysing coupled R\"ossler attractors with a known delay, that the method yields satisfactory results. Then, we apply this method to human pathologic tremor. The delay between simultaneously measured traces of Electroencephalogram (EEG) and Electromyogram (EMG) data of subjects with essential hand tremor is calculated. We find that there is a delay of 11-27 milli-seconds ($ms$) between the tremor correlated parts (cortex) of the brain (EEG) and the trembling hand (EMG) which is in agreement with the experimentally observed delay value of 15 $ms$ for the cortico-muscular conduction time. By surrogate analysis we calculate error-bars of the estimated delay.Comment: 21 pages, 8 figures, elstart.cls file included. Accepted for publication in Physica

Relation between post-movement-beta-synchronisation and corticomuscular coherence [Abstract]

Objective: To analyse post-movement-beta-synchronisation in the EEG and EEG-EMG coherence simultaneously. Background: The mechanisms and function of EEG synchronistion in the beta-band after the end of a short movement is not clear. The corticomusucular coupling during isometric muscle contractions occurs in the same beta-band. It is unclear however, if these two features of cortical motor physiology are related. Methods: 64-channel EEG was measured simultaneously with surface EMG of the right FDI-muscle in 11 healthy volunteers. Subjects kept a constant medium strength contraction of the FDI-muscle during the entire experiment. Superimposed on this they performed repetitive self-paced brisk short contractions. Time-frequency analysis including coherence over time was performed with respect to the onset of the brisk movements and averaged for 40 contrcations in each subject. Results: Post-movement-beta synchronisation (PMBS) was found in the contralateral electrodes C1, C3 and C5 with a maximum 1-2.5sec. after the brisk movements in the frequency range between 16 and 27 Hz for all the subjects. In 9 of the subjects there was coherence between the EEG recorded from these electrodes and the FDI in the same frequency range as the PMBS and with the maximum occuring at the same time. The other two subjects did not show any corticomuscular coherence. Conclusions: Post-movement-beta-synchronisation coincides with corticomuscular coherence in the same frequency band. Thus PMBS is not merely a cortical phenomen but seems to involve the whole corticomuscular system, possibly reflecting recalibration after brisk movements

Network for parallel gamma synchronizations during upper limb movement [Abstract]

Objective: Our aim was to identify the sources of parallel gamma synchronizations (GS) and analyze the direction of information flow in their network, at the beginning of simple and combined upper limb movements. Background: GS at the onset of movements may promote the processing between functionally related cortico-subcortical neural populations. Methods: We measured 64-channel EEG in 11 healthy volunteers; surface EMG detected the movements of the dominant hand. In Task1 subjects kept a constant medium-strength contraction of the first dorsal interosseus muscle and superimposed on this they performed a repetitive voluntary self-paced brisk squeeze of an object. They executed brisk contraction in Task2 and constant contraction in Task3. Time-frequency analysis of the EEG signal was performed with multitaper method. GS sources were identified in five frequency bands (30-49Hz, 51-75Hz, 76-100Hz, 101-125Hz and 126-150Hz) with the beamformer inverse solution dynamic imaging of coherent sources by taking the EMG as the reference signal. The direction of information flow between the sources was estimated by renormalized partial directed coherence for each frequency band. To identify significant connections, the data driven surrogate test and the time reversal technique was performed. Results: The first three sources in consecutive order in each movement task, in every frequency band, were as follows: contralateral primary sensorimotor cortex (S1M1), dorsolateral prefrontal cortex (dPFC) and supplementary motor cortex (SMA). Gamma activity was detected in narrower low- and high-frequency bands in the contralateral thalamus (TH) and ipsilateral cerebellum (C), in all three tasks. In the combined Task1 additional low gamma activity appeared in the contralateral posterior parietal cortex (PPC). In every task, S1M1 had efferent information flow to the SMA and the dPFC; the latter had no afferent relation to the network. S1M1 and SMA had a bidirectional connection with the TH, and the C. Afferent information flow was detected from the PPC to the SMA and bidirectional flow between PPC and the TH, in the combined Task1. Conclusions: The same network could be identified for the parallel gamma synchronizations in the tasks; it was complemented by the PPC in the combined Task1. S1M1 drove the other cortical sources and had afferent activity from the TH and the C, which activated in variable frequency bands in the tasks

Differentiating Parkinson's disease from advanced essential tremor using EMG [Abstract]

Objective: The primary objective of this study is to find out the appropriate spectral parameter from the electromyography (EMG) signals which quantitatively classify PD from ET tremor. Background: Essential tremor (ET) and the tremor in Parkinson's disease (PD) are the two most common pathological tremor forms encountered in clinical practice. Differential diagnosis between the two tremors is usually achieved clinically. There is a certain overlap in the presentation of these tremor forms while the clinical differentiation on purely clinical grounds might be challenging. Methods: EMG recordings on the more affected side of 194 age (60.84 ± 8.9 years) and sex matched (M=120; F=74) ET and PD patients were analyzed. Postural tremor was recorded from the more affected side, while subjects extended their hands and fingers actively to a 0 ° position with the resting forearm. This posture was held against gravity, and in this condition the tremor was recorded for a period of 30 seconds. The parameters from the power spectra of the EMG were tremor frequency, peak power, number of harmonic peaks, waveform asymmetry (autocorrelation decay), mean peak power of all harmonic peaks, coherence (antagonistic muscles), frequency-frequency coupling, phase-phase coupling and power-power coupling were estimated on the basic and harmonic frequencies. The reliable parameter for the classification was quantified with a support vector machine (SVM) classifier (50% training; 50% testing). Results: Tremor frequency, peak power, number of harmonic peaks showed less than 50% classification accuracy for the testing data. Whereas mean peak power of all harmonics showed 93% classification accuracy for the discrimination (PD>ET), frequency-frequency coupling showed 95% (ET>PD), phase-phase coupling showed 96% (ET>PD), power-power coupling showed 98% (PD>ET) respectively. Conclusions: The relation between the basic and harmonics peak frequencies play a major role in distinguishing ET from PD tremor. The mean peak power of all harmonics and the three coupling estimates are applicable measures to separate by the aid of artificial learning algorithms clinical difficult entities of ET from PD tremor cases with a very high reliability

Cortical representation of voluntary and non-voluntary motor rhythms [Abstract]

Background: Coupled bilateral cortical activity seems to be the basis for intermanual coordination, but its direct relation to the peripheral bimanual movements is still unclear. Methods: We analyzed corticomuscular coherence between 64-channel EEG and bilateral hand/finger extensor and flexor EMG and intermuscular coherence between left and right muscle activity in 18 healthy subjects during unilateral and bilateral fast rhythmic hand/finger movements and isometric contractions on both sides. Results: Partial coherence between two separated coherent areas and muscle and corticomusuclar/cortico-cortical delays were calculated. Bilateral voluntary rhythms of each hand showed coherence with lateral cortical areas on both sides in 60-80% of the recordings and occasionally with the frontal midline region (10-30%). They were always coherent between both hands. Unilateral rhythmic movements were represented in the ipsilateral cortex in only 20%-30% of the recordings tending to be more frequent with the left hand, paralleled by more frequent left-right muscle coherence. Partial corticomuscular coherence was most often abolished (p� 0.05) when the cortical signal contralateral to the coherent muscle was used as the predictor indicating that the ipsilateral and occasional frontomesial connection with the muscle was mainly indirect via the contralateral cortex. Cortico-cortical delays showed mainly bidirectional interaction at the movement frequency and were bimodally distributed ranging between 1-10 ms and 15-30 ms indicating direct cortical and subcortical routes. Corticomuscular delays ranged mainly between 12-25 ms indicating fast corticospinal projections, and musculocortical feedback showed similar delays. These corticomuscular delays were not significantly different for the 15-30 Hz coherence encountered in 60-70% of the recordings during isometric contractions. However this involuntary corticomuscular rhythm was strictly unilaterally represented and did not show coherence between left and right muscles. Conclusions: We conclude that there is a fundamental difference between the complex bilateral cortical network representing and controlling a voluntary motor rhythm and the cortical representation of non-voluntary 15-30 Hz rhythm as well as pathological non-voluntary rhythms likeorganic tremors