Piper rhythm of the electromyograms of the abductor pollicis brevis muscle during isometric contractions

Journal of Electromyography and Kinesiology 21 (2011) 184–189

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Piper rhythm of the electromyograms of the abductor pollicis brevis muscle during isometric contractions Vinzenz von Tscharner a,⇑, Marina Barandun b, Lisa M. Stirling a a b

Human Performance Laboratory, University of Calgary, Calgary, Alberta, Canada Department of Plastic and Reconstructive Surgery, University Hospital Basel, Switzerland

a r t i c l e

i n f o

Article history: Received 6 November 2009 Received in revised form 10 October 2010 Accepted 11 October 2010

Keywords: Hand EMG Carpal tunnel syndrome Wavelet analysis Beta band Gamma band Fatigue

a b s t r a c t A temporal pattern coding, synchronization and rhythmicity form an integral part of central nervous system information controlling the muscle activation. Rhythmic oscillations of muscles at frequencies of 35– 60 Hz were already noted in the electromyograms by Piper (1907). The purpose of this study was to resolve the Piper rhythm in the EMG of the APB muscle and report the pacing frequencies of the Piper rhythm. The Piper rhythm was identified using the power of the EMG signals extracted by a wavelet transform at higher frequencies (170–271 Hz). The results showed distinct power of the intensity extracted by the wavelets in a frequency band ranging from about 30–60 Hz. The band was reflected in the power spectra of the EMG intensity and in the first eigenvector of a principal component analysis of the power spectra. The fact that the Piper rhythm shown in this study for the APB muscle yielded a large contribution to the total power means that one can use the frequency and amplitude of the Piper rhythm in future analysis of EMG signals to monitor the influence and changes of the central command. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction A general framework that views temporal pattern coding, synchronization and rhythmicity as an integral part of central nervous system information processing seems to form the bases for muscle activation (Farmer, 1999). Rhythmic oscillations of muscles at frequencies of 35–60 Hz were already noted in the electromyograms by Piper (1907). Stochastic models used in ergonomics and kinesiology consider the surface EMG to be generated by a stochastic process whose amplitude is related to the level of muscle activation and whose power spectral density reflects muscle fiber conduction velocity (McGill, 2004). Other models represent realizations of zero mean, nonstationary, mutually uncorrelated, random processes (Farina et al., 2008). However, other models include synchronization of motor unit action potentials (MUAP) which may induce rhythms (Stegeman et al., 2000). Rhythms seem to be present as well at low and high forces. Piper rhythms are not dependent on the presence of strong stretch reflexes (equally prominent in muscles with brisk, weak or absent stretch reflexes) and seem to depend on some kind of pace-maker in the spinal cord or the cerebrum which tends to entrain and synchronize motor impulses (Hagbarth et al., 1983). ⇑ Corresponding author. Address: Human Performance Laboratory, University of Calgary, 2500 University Drive, Calgary, Alberta, Canada T2N 1N4. E-mail address: [email protected] (V. von Tscharner). 1050-6411/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.jelekin.2010.10.004

The brain’s control center for synchronizing the muscle activation is the motor cortex. The rhythmicity between and within muscles was detected in the in the frequency ranges of the alpha (7–13 Hz), beta (17–23 Hz) and gamma (35–41 Hz) bands of the brain activity (Farmer et al., 1993; Conway et al., 1995; Salenius et al., 1996, 1997) The spectra of rectified EMG occasionally revealed a band at 20 and at 40 Hz and satisfactory coherence between the brain activity and the EMG was often obtained indicating a rhythmicity in the EMG but not showing it explicitly. Various approaches can be taken to measure rhythmicity in the EMG. Forces with respect to the maximum voluntary contraction are often considered by researchers interested in rhythmicity (Andrykiewicz et al., 2007; Brown, 2000; Conway et al., 1995; Mima et al., 1999) whereas, measures of EMG amplitude were usually not explicitly quantified. If one wants to measure the rhythm in an EMG one should concentrate on higher frequencies only, because they will be present whenever a motor unit (MU) is activated but will not interfere with the lower frequency bands associated with electroencephalograms (EEG) or magnetoencephalograms (MEG). The decomposition of the EMG’s power in time and frequency is preferably done using a wavelet transform because time resolution is kept short (Beck et al., 2008; Coorevits et al., 2008; von Tscharner, 2000). Time and frequency resolutions are critical if one wants to address rhythmicity. The non-linearly scaled wavelets that are used in this study have the advantage of having an appropriate time resolution and a narrower bandwidth of the

V. von Tscharner et al. / Journal of Electromyography and Kinesiology 21 (2011) 184–189

wavelets at higher frequencies than classical linearly scaled wavelets (von Tscharner, 2000). We previously studied the decay of conduction velocity and mean frequency of the abductor pollicis brevis (APB) muscle with fatigue but without paying special attention to rhythmicity (Barandun et al., 2009). Results indicating the change in frequency of rhythmicity with fatigue are contradictory (Yang et al., 2009; Tecchio et al., 2006). A decay of conduction velocity and mean frequency also results from muscle atrophy associated with carpal tunnel syndrome (Kulick et al., 1986; MacDermid and Wessel, 2004; Rainoldi et al., 2008). It follows that rhythmicity is always present in one of the frequency bands due to the pulse-based manner in which muscles are controlled. However, the gamma band activity was less frequently observed than the beta band activity. During a static force condition the well-documented beta-range corticomuscular coherence (15–30 Hz) with the contralateral sensorimotor cortex was reported (Andrykiewicz et al., 2007). Gamma band corticomuscular coherence (30–45 Hz) occurred in both small and large dynamic force conditions without any significant difference between both conditions. In the right forearm a rhythmicity of 45 Hz and a down shift from the gamma to the beta band activation was observed when reducing the force levels from the maximum voluntary contraction to around 20–40% (Brown et al., 1998). The Piper band (30–60 Hz) was most evident during strong isometric contractions and correlated to the brain activity during movement (Brown, 2000). For the APB muscle under investigation in this study, a rhythmicity of 40 Hz was typically observed during strong contractions (Mima and Hallett, 1999; Mima et al., 2001). It was thus shown that there were rhythms in the EMG which correlate with rhythms in the brain activity but the amplitudes and frequencies of the rhythms in the EMG were not explicitly resolved. The purpose of this study was to explicitly resolve the rhythmicity (Piper rhythm) in the EMG of the APB muscle. The rhythms were defined as repetitive (non random) bursts of MUAPs creating oscillations of the power of the EMG signal. The oscillations were identified using the power of the EMG signals extracted by the wavelet transform at higher frequencies. This technique has, to our knowledge, not been used previously and yields a signal reflecting the rhythm. A measurement of the rhythm in the EMG will allow observing the effects of one aspect of central control, the pacing of the muscles, independently of measuring brain activity. It will provide an alternative to using EMG amplitude measurements that have been questioned (Farina et al., 2004). The intensity of the EMG signal obtained by the wavelet analysis may provide an alternative signal to the rectified EMG for measuring corticomuscular coherence and thus help in neurophysiologists in their research. 2. Methods 2.1. The subjects The study was approved by the Conjoint Health Research Ethics Board of the University of Calgary. Informed consent was obtained from 14 healthy, right handed subjects (7 females and 7 males, average age 43 years) who participated in this study. Of these only those 13 were used where both hands yielded 6 trials of analyzable data (26 hands). 2.2. Experimental setup Details of the experimental setup were reported previously (Barandun et al., 2009) and the essential parts for this study are summarized below. The skin covering the APB muscle was washed with water and soap, lightly abraded and cleaned with

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alcohol. A linear array of five Ag-electrodes (inter electrode distance 6 mm; diameter 2 mm) placed above the muscle belly parallel to the muscle fibers allowed EMG signals to be recorded at 10 kHz (bandwidth 10–700 Hz) from four adjacent electrode pairs. Two adjacent electrode pairs (3 electrodes) were selected if the two bipolar EMG recordings showed a clear correlation thus indicating that they were placed between the innervation zone and the muscle tendon interface. The EMG signal from the electrodes that were closer to the innervation zone was used for the analysis. Force and EMG measurements from a hand placed in an intrinsic plus position were displayed on a screen and recorded during maximal voluntary contraction. Subjects performed six trials with a rest interval of 2 min in between. From the measurement a period lasting 1.64 s was selected for analysis (sub dividable in 4 sequences of 4096 points) starting 0.3 s after maximal voluntary contraction was reached. This time was short enough to consider the signal as stationary and not significantly affected by fatigue. 2.3. Estimation of a mimicked MUAP and simulation of an EMG The EMG is often explained as superposition of multiple MUAPs that occur at random instances (interference EMG). A simulated EMG is computed by convolving a modeled MUAP with a pulse train reflecting the random instances (Hermens et al., 1992). The power spectra of a modeled MUAP and the resulting simulated EMG are identical in shape, however, the power spectrum of the simulated EMG contains additional noise. In this study, we reversed this procedure to compute a mimicked MUAP from a recorded EMG. The power spectrum from the raw EMG signal was computed using the sequential Fourier transform (using 32 sequences of 512 points each) (Rosenberg et al., 1989). An inverse Fourier transform of its square root multiplied by i yielded a symmetric, mimicked MUAP. A new, simulated EMG was then computed by convolving the mimicked MUAP with a randomly distributed pulse train of 2000 pulses. Its amplitude was adjusted to recover the energy of the raw EMG signal. If there was any rhythm in the raw EMG, this rhythm will be eliminated in the simulated EMG. However, the general characteristics of the raw EMG will be closely reproduced and the power spectra of the measured and simulated EMGs will be identical. A simulated EMG was created for each measured EMG and was used as a reference signal containing no rhythmicity. 2.4. Intensities of the EMG signal extracted by the wavelet transform A set of non-linearly scaled slightly modified Cauchy type wavelets were used to extract the EMG intensity (von Tscharner, 2000; Barandun et al., 2009). The wavelets were characterized by their center frequencies (cf: 7, 19, 38, 62, 92, 128, 170, 218, 271, 331, 395, 466 and 542 Hz). The wavelet transform yields the power of the EMG signal at each time point subdivided into the frequency bands covered by each wavelet. In this study the power recovered by the wavelets with the center frequencies 170–271 Hz were used to measure the EMG intensity representing the presence of the EMG signal. The low frequencies were eliminated because the long time resolutions of the corresponding wavelets may mask shorter rhythms. The higher frequencies were eliminated because they recover increasing amounts of power from the noise. From each hand and for each trial the intensities were computed for the EMGs and the simulated EMGs. The power spectrum of the EMG intensity was computed by a sequential Fourier Transform using 4 sequences of 4096 points yielding a 2.44 Hz frequency resolution. The resolution was selected to allow discriminating alpha and beta bands if they were present. The power spectrum of one hand was obtained by averag-

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ing the power spectra of 6 trials. The averaged spectra were normalized dividing them by the total power. They were assigned to two groups containing the measured and simulated spectra, M and S respectively. Each of these groups contained the power spectra of 26 hands.

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PCAs were conducted using M, S and the joint M and S spectra (Ramsay and Silverman, 1997). The PCA computes the eigenvalues, the PC-vectors (PCn) and the PC-scores (projections of the spectra onto the PC-vectors). The eigenvalues form a descending series of values. They were normalized by dividing them by the sum of the eigenvalues of PC3–PC13 assuming that these represent variance created predominantly by noise. A scaling factor between recorded and simulated data was defined as the corresponding ratio of the normalizing sums of their eigenvalues (eigenvalues of PC3– PC13) and was used to rescale the averaged power spectra of the non-simulated condition. If we assume that the differences between power spectra were only caused by noise then the extrapolation of the trend of the eigenvalues to the position of the first eigenvalue yields an estimation of its value (see results in Fig. 2). Therefore, for each PCA performed, an extrapolated first eigenvalue was found by back-extrapolating the trend determined from the higher indexed eigenvalues. PC1 was considered to contain information that distinguishes between the various power spectra of the hands if the first eigenvalue of the M was higher than the first eigenvalue of the S, and also higher than the back-extrapolated one. The standard deviation of the first eigenvalue was computed from the S. The simulations were repeated 12 times yielding a mean first eigenvalue and its standard deviation. We assumed that the standard deviation of the first eigenvalue of M is about the same. The standard deviation of the difference was thus the computed standard deviation times the square root of 2. The difference was deemed significant at the 95% level of confidence if it was larger than 2 standard deviations. PC1-scores will be used to assess whether there was a systematic difference between M and S. The hypothesis was that the mean PC1-scores of the M and S are identical. If the hypothesis was falsified at the 95% level by a paired two sided Student’s t-test then there is a significant difference between these spectra representing the rhythmicity.

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2.5. Analysis of the power spectra by a principal component analysis (PCA)

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Fig. 1. (a) The rectified raw EMG signal of hand 19 (trial 3) is shown for a time period of 0.3 s. Superimposed is the line representing the intensity extracted by wavelets 7–9. (b) The simulated EMG of hand 19 (trial 3) is shown for the same time period.

3.2. Analysis of the power spectra 3.2.1. PCA of the M spectra The power spectra of the EMG intensity, M, were submitted to a PCA. This analysis should show whether there was a non random main difference between hands. The first goal of the PCA analysis of M was to assess whether the eigenvalues indicated that there was a component in the signal that was not likely random. The series of eigenvalues that resulted from the PCA are shown in Fig. 2. The expected first eigenvalue of the M was found by extrapolating the trend from the higher indexed eigenvalues to index one. The measured first eigenvalue was 48% and was distinctly higher than the extrapolated one which was 27%. For a comparison, the PCA analysis was done for the simulated spectra, S. In this case in the power spectra only differed in their noise. The first eingenvalue of the S was 27.97% and its standard deviation was 0.43%. No standard deviation can be obtained for the first eigenvector derived from the M, however, we expected it to be of the same order of magnitude. Thus the difference between the first eigenvalue of the measured M and of the simulated S was 20% of the sum of the eigenvalues 3–13 and the error of the difference was estimated to be about 1%. One can infer that the first eigenvalue of the M was significantly larger than the one of the simulated spectra. Thus according to both criteria the first eigenvalue for M was signifi-

3. Results

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The pulsation caused by the Piper rhythm was apparent in the rectified raw EMG signal especially when the envelope representing the EMG intensity was superimposed. In Fig. 1a a period of 0.3 s of the rectified raw EMG signal is shown together with the intensity that was extracted by wavelets. In contrast, a stochastic superposition of MUAPs as computed by the model calculation leads to fluctuations of the EMG intensity that are not regular but may resemble a rhythmic pattern (Fig. 1b). By a visual inspection one cannot determine whether the intensities really reflect rhythms or are an effect of the random superposition of MUAPs. A systematic analysis was therefore required to differentiate between rhythmic fluctuations of the EMG intensity and random fluctuations. The analysis was done in the frequency domain using the properties of the Fourier transform.

Eigenvalues %

3.1. Inspection of the raw EMG signal and its intensity

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0 0 5 10 Index of eigenvalues Fig. 2. Series of eigenvalues normalized to the sum of the eigenvalues 3–13. Dots represent eigenvalues from the measured spectra, M, and diamonds for the simulated data, S.

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3.2.2. Comparison of averaged M and S The PCA revealed that for the M there is a distinct band in the power spectra between 30 and 45 Hz. One can expect that the band should also appear in the mean across the power spectra of all hands. The mean power spectrum of the M multiplied by the scale factor computed from the normalization by the eigenvalues of PC3– PC13 (1.15) and of the S are shown in Fig. 4. Here the band that represents the difference between the power spectra ranges from about 30–65 Hz. Without the multiplication by the scale factor the difference would be negative between 10 and 30 Hz and thus resemble PC1-vector shown in Fig. 3a. The power of the difference relative to the power in the power spectrum of the simulated spectra was 19.9%. In other words the rhythmicity added additional power to the power spectrum.

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Fig. 4. Mean of the power spectra of all hands, M (dashed line) and of the simulated data, S (solid line). The difference between the two power spectra is shown by the diamonds and reveals the band between 30 and about 65 Hz. The line following the diamonds represents the PC1 multiplied by the averaged PC1-scores obtained in Section 3.2.3.

3.2.3. PCA of the M and S The averaged power spectra shown in Fig. 4 do not allow knowing whether the EMGs of an individual hand contained substantial power from the rhythm. To obtain a more detailed view of the power contribution to the EMG a PCA was performed on both, the measured and simulated EMGs. As in Section 3.2.2 all M were multiplied by the scaling factor (1.15) thus adjusting for the normalization difference before performing the PCA. The PC1 in this case reflects the main difference between measured and simulated spectra and is shown in Fig. 5. It shows a clear band between 30 and about 60 Hz. The PC1-scores computed by the projection of the individual M and S onto PC1 represent how much PC1 contributes to the measured spectrum. The PC1-scores are shown in Fig. 6 for the measured and for the simulated spectra. The PC1-scores were higher for the M then for the S for all hands. On average the PC1-scores were 0.048 higher and a two tailed t-test showed that the difference was highly significant (p  0.01). In all hands the PC1-scores were higher for the measured than for the simulated data indicating that a rhythm was always present. In fact, the two groups were almost fully separable (25 out of 26 hands) based on their PC1-scores. However, in some hands the rhythm was more pronounced. The PC1 multiplied by the averaged PC1-scores was drawn as a line superimposed to the diamonds into Fig. 4. It shows that the separation was based on the difference of the averaged spectra. Knowing that on average the additional power was 19.9% one can use the PC1-scores to estimate the fraction of additional power of the power spectrum of an individual hand. The minimum PC1-score difference for a single

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cantly higher than expected for spectra containing a random variability only. Therefore the shape of PC1 of M (shown in Fig. 3a) contains information representing the part of the power spectrum that distinguishes between the various power spectra, M, of the hands. The shape of the PC1 of M reveals a distinct band in the frequency range between 30 and 45 Hz. The trace has negative values from 10 to 30 Hz. This is a result of the normalization of the power spectra because what’s added in one frequency range has to be removed in another one to keep normalization constant. The PC1 of the S shows a more random distribution of values and the band between 30 and 40 Hz is not present (Fig. 3b). The band was also not present in higher order PC-vectors. The result of the PCA on M spectra revealed that there is an aspect of the power spectrum represented by PC1 that is different between the power spectra of the various hands.

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Fig. 3. (a) Representation of the vector PC1 of the power spectra in M obtained from the measured EMG (thick solid line) and of the scaled, inverted mean of the simulated power spectra (thin line). The amplitude results from the fact that the norm of a PC-vector is 1. (b) Shape of the vector PC1 for the power spectra in S of the simulated EMG.

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Fig. 5. Representation of the vector PC1 of the power spectra of the PCA on the combined spectra M and S.

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PC1-scores

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Hands ranked according to PC1-scores Fig. 6. PC1-scores computed by the projection of the individual M and S onto PC1 shown in Fig. 5. Dots represent scores obtained from measured data and squares from simulated ones. The dashed line is two standard deviation above the solid line which represents the mean of the scores of the simulated data.

hand was 0.018 and the maximum 0.089 thus indicating that the rhythm contributed between 7% and 36.9% of the power. 4. Discussion The results showed distinct power of the intensity in a frequency band ranging from about 30 to about 60 Hz which was caused by a rhythmicity of the EMG intensity. The band was reflected in the power spectra of the EMG intensity in PC1 of the power spectra of the M and in the PC1 of the combined spectra, M and S. This was an indication that this band reflected rhythmicity in the EMG of all hands but with individually varying power. The band was called the Piper band because the pacing frequency was in the range reported for the Piper rhythm (Brown, 2000) and it occurred in the APB muscle where the corticomuscular interaction has been well established (Mima and Hallett, 1999; Mima et al., 2000). To our knowledge, there is limited information regarding the features of the Piper rhythm against which we could compare our work. The discussion is therefore focused on the interpretation of the information obtained in this study. The band reflecting the rhythmicity was extracted by the PCA of the M without using the simulated data. It should be noted that because the spectra in M were normalized the detection of the rhythms is not dependent on the amplitude of the EMG signal and thus not affected by amplitude cancellation. The simulated spectra were used to refine the detection and quantify the power differences. The PC1 of M reflects the fact that when the contribution from the Piper band was large then, because of the normalization, the contribution from the remaining power spectrum was smaller (Fig. 3a). The mean power spectrum of the simulated data shown in Fig. 4 yields a base line shown in Fig. 3a. The difference between the baseline and PC1 represents the actual changes caused by the rhythms. With respect to both, this baseline or a horizontal baseline, the result from the PCA revealed a positive band. One could speculate that there is a much smaller negative band between 45 and 55 Hz. We interpret the band occurring at lower frequencies as the beta band and the one occurring at the higher frequencies may reflect the much weaker gamma band. Thus when a subject had a strong beta band activity their gamma band activity seemed correspondingly reduced. When both bands are present they both contribute power to the power spectra. This explains why the PC1 of the combined M and S shown in Fig. 5 is broader and covers the full range, 28 to about 60 Hz, encompassing both, the beta and the gamma band and covers the full range of the Piper rhythm. The findings indicate that the activity in the Piper band was present in all subjects and its amplitude was hand specific. The difference between hands reflects how strongly the subjects synchronized the

MUAP by the central command. One has therefore to differentiate between some MUAP that occur in a stochastic order, those that occur in a rhythmic order and those that occur in a synchronized way e.g. during an explosive movement (Merlo et al., 2005). Both the beta and the gamma bands discussed above contribute additional power to the power spectrum of the intensity (Fig. 4). This additional power was not sufficient to create distinct peaks in the spectrum but became visible as additional bands. This part of the power spectrum disappeared when the raw EMG signal was simulated by randomly distributed, mimicked MUAPs. This is an indication that the power of the additional band was caused by the synchronization of the MUAPs. The fact that the power spectrum has a side band in the frequency range of the Piper rhythm is not sufficient to conclude that the rhythm was present. This conclusion can only be drawn after demonstrating that a simulated EMG consisting of a random superposition of mimicked MUAP does not show this side band. The analysis of the rhythmicity has mainly been done in the frequency domain where coherence represents a very sensitive method for detecting even minute synchronization effects. Farmer et al. (1993) have used cross-intensity (cross correlation) as a measure of synchronization of motor unit activation in the time domain. They reported a central cross correlation peak. The typical non central peaks that occur in cross correlated rhythmical signals did not reach the level of significance in their study. This rhythmicity only occurs in a reliable and repeatable way if many MUAPs are synchronized by a central command. Our primary assumption was that one characteristic pacing frequency would be typical for the Piper rhythm. The difference in the PC1 of M and of the combined M and S show that both, the beta and a gamma band had to be considered. Although the pacing frequency in most hands is more likely associated with beta band activity, we cannot exclude that the higher pacing frequencies were associated with the gamma band. Thus the trace of the EMG intensity (envelope in Fig. 1a) will show a superposition of beta and gamma band oscillations thus yielding a less distinct pattern of the rhythm. In summary, one can conclude that the spectra reflecting the pacing frequencies covered the whole frequency range of the Piper band including the beta and the gamma band frequencies. The fact that the Piper rhythm shown in this study for the APB muscle yielded a large contribution of almost 20% of the total power means that one should not neglect the rhythmicity in future analysis of EMG signals or when modeling EMG signals. Synchronization of MUAPs is not the exception but the rule. In fact, we also found rhythms in the EMG of the gastrocnemius muscle of runners (Stirling et al., 2010). The possibility to observe the Piper rhythm directly in the EMG without using the coherence with the brain activity opens the possibility to study the behavior of central control in the peripheral signal. This will be of special value when studying EMG signals obtained during fatigue (Tecchio et al., 2006; Holtermann et al., 2009; Yang et al., 2009), or during fatiguing movements, for instance when playing with a racquet (Girard and Millet, 2008). In our view, the rhythmicity of the EMG seems to reflect a pacing of the muscle once it is activated. One could consider this pacing as a fine tuning of the muscle activation which allows subtle adjustments of the exerted forces, changing a preferred, preprogrammed activation in response to exterior influences. Thus measuring changes of rhythmicity in the EMG may become useful in unraveling new neuromuscular and corticomuscular aspects involved in controlling human movements.

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Vinzenz von Tscharner was born in Switzerland 1947. He received his diploma in applied physics and mathematics 1974 and his PhD degree in biophysics at the University of Basel, Switzerland. He was a post doctorate fellow at Oxford University, Dept. Biochemistry, England in 1978 and 1979, and a post doctorate fellow at Stanford University, California USA Dept. Biochemistry 1998. He returned to the Biocenter in Basel 1981. He was then research affiliate at the Theodor Kocher Institute in Bern and specialized in signal transduction studying cellular responses related to cytokin binding. He became Adj. Assistant Professor (1997) and Adj. Associate Professor (2000) at the Human Performance Laboratory, University of Calgary. His main field of research is the signal propagation controlling movement patterns of humans. This involves biophysical/biomedical measurements and the analysis of sensory systems.

Marina Barandun graduated from Medical school at the University of Zurich, Switzerland in 2004. In 2005, she obtained a visiting doctor research fellowship from the Department of Kinesiology, University of Calgary, Canada. During her residency, she completed 2 years of common trunk in General Surgery at the Triemli Hospital in Zurich, Switzerland, followed by 1 year of Hand Surgery at the Kantonsspital Liestal, Switzerland. Currently, she’s a resident at the Department of Plastic, Reconstructive, Aesthetic and Hand Surgery at the University Hospital of Basel, Switzerland.

Lisa M. Stirling (née Guevremont) received her B.A.Sc. degree (with honours) in electrical engineering from the University of Toronto, Canada, in 2002. She completed her Ph.D. in medical sciences (biomedical engineering) at the University of Alberta, Canada, in 2007. Her graduate research focused on the development of control algorithms and functional electrical stimulation protocols for restoring standing and stepping after spinal cord injury. She is currently a post-doctoral fellow at the Human Performance Laboratory, University of Calgary, where she is pursuing her interests in the neural control of movement and the application of engineering approaches to the fields of rehabilitation and movement science.