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Subject code Name of the Subject. Enzyme components - Mechanism of enzyme reactions - Factors influencing enzymatic activity. detection and recovery of fungi. · The human hand can perform an enormous range of movements with great dexterity. Some common everyday actions, such as grasping a coffee cup, involve the.
A synergy- based hand control is encoded in human motor cortical areas[Editors’ note: the author responses to the first round of peer review follow.]In addition, if the authors choose to resubmit the manuscript in a new form to e. Life, the other comments of the reviewers should be addressed. In post review discussion, all reviewers agreed with the suggestion that noise ceilings should be reported. Reviewer #1: The study "A synergy- based control is encoded in human motor cortical areas" provides an investigation of the MRI patterns associated with the execution of grasp- like hand shapes. The main finding is that the activity patterns of two left - out postures can be better discriminated when using 5 regressors extracted from kinematic synergies than 5 regressors reflecting the unsigned displacement of individual fingers or 5 regressors picked from features of EMG recording of 5 different hand muscles. The conclusion of the study are largely overlapping with that of an earlier paper from our lab (Ejaz et al., 2.
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The current version of the paper, however, has a number of weaknesses that certainly would need addressing. The alternative models (individual fingers and muscle) give the appearance of straw men. The individual finger uses the L1 norm of movement of each finger – so in contrast to the kinematic synergy model, it does not distinguish between finger flexion and finger extension. This decision appears to be somewhat arbitrary. So, it leaves the reader with the question of whether there is something special about taking the absolute value, or about the specific rotation of these 5 factors in representational space.
A more convincing line of investigation would be to try to use optimisation to rotated the 5 linear factors in the kinematic space as to get the best possible decoding performance, and then test the closeness of this solution with the one provided by the kinematic synergy model. The Reviewer is absolutely correct in stating that finger flexion and extension fully deserve to be kept as distinct as possible. As a matter of fact, our individual digit model was actually obtained by summing the individual joint angles for each digit, though in the description in the text we erroneously reported that it was the L1- norm of each digit. Consequently, our original individual digit model already distinguished positive and negative joint angles (with respect to the resting posture), thus preserving the difference between flexion and extension. We apologize for this erroneously reported piece of information.
Matters are worse with the muscle model. The authors recorded 5 muscles only, despite the fact that in our experience it is feasible to get 1.
Ejaz et al., 2. 01. These may not always reflect individual muscles, but that is hardly important if we only want to obtain a representative picture of the space of muscle activity. Previous reports indicate that a reliable gesture discrimination can be achieved from seven (Weiss & Flanders, 2.
Shyu et al., 2. 00. Ganesh et al., 2. Ahsan et al., 2. 01. EMG data are notoriously prone to artifacts such as cross- talk or amplitude cancellation (i. In our study, both the number of recorded muscles and the analyses performed were able to guarantee a reliable discrimination between gestures, as indicated by the rank accuracy measure, which was well above the chance level for each subject (Supplementary file 1. M and Figure 6). In most advanced EMG devices, the number of acquisition sites on the forearm can raise dramatically up to 1. Gazzoni et al., 2.
Muceli et al., 2. However, due to the above mentioned cross- talk, the effect of the potential benefit that could derive from an increased number of EMG electrodes is still much debated: indeed multi- channel setups show a high collinearity, since many channels inevitably record the same muscles. As a matter of fact, a recent work that directly compared multiple channel setups – using either 6, 8, 1. Muceli et al., 2. Nevertheless, in order to explore directly the Reviewer’s critique, we tested an independent sample of four healthy young subjects who performed the very same task used in our current paper to verify the impact of the number of EMG channels on the ‘muscle’ model. To this aim, for each subject we acquired six runs, each comprising twenty trials of delayed grasp- to- use motor acts towards visually- presented objects.
Data were acquired using a Bagnoli 1. EMG recording device (Delsys Inc, Natick, MA, USA). Sixteen electrodes were placed on the hand and forearm using the same placement adopted in our protocol (see revised Methods and Appendix figure 1) as well as in three distinct protocols with different spatial resolutions (Bitzer and van der Smagt, 2.
Ganesh et al., 2. Ejaz et al., 2. 01. To obtain an overall estimation of the impact of the number of EMG recording sites and different preprocessing steps, data were analyzed using two distinct procedures: the first suggested by the Reviewer (Ejaz et al., 2. Methods. Both preprocessing procedures were assessed with a leave- one- out cross- validation algorithm. Using the first procedure (Ejaz et al., 2. EMG channels (acquired at 1,0. Hz) were de- trended, rectified, and low- pass filtered (fourth- order Butterworth filter, 4.
Hz). The time series from each gesture and channel were later averaged over a 2. We obtained twenty 1. Then, we tested the discriminability of each individual movement (probe element) from each run against all the movement vectors averaged across the five remaining runs (rest dataset). This was performed with a leave- one- out rank accuracy procedure (Mitchell et al., 2.
Mahalanobis distance. If the distance between the probe element and the vector from the rest dataset, which represents the same gesture, is lower than the distances between the probe vector and the other elements of the rest dataset, one may state that the element can be discriminated. The accuracies were tested against null distributions of 1. This rank accuracy procedure provides a measure of the quality of each individual channel configuration: the higher the accuracy, the more informative the configuration. Since we wanted to estimate the discriminability as a function of the number of channels, we performed two tests, whose results are shown in Appendix figure 2. First, we generated all the possible configurations that could be obtained by choosing the channels randomly.
The rank accuracy procedure was performed for each of these channel configurations. The result of this procedure is shown in Appendix figure 2 (red line). Second, we selected four subsamples of electrodes (displayed in Appendix figure 1; electrodes 1- 4, 6- 8, 1.
Ejaz et al., 2. 01. Results for each configuration, averaged across subjects, are represented by the orange dots in Appendix figure 2. In the feature- based procedure, we computed, for each trial, eighty- two features from each channel, as described in the Methods section of our manuscript. Each individual gesture was then described as a combination of five PCs (muscle synergies), extracted from the features pooled across channels.
Subsequently, a machine learning procedure based on a rank accuracy measure was employed to test to what extent the gestures could be discriminated based on the five muscle synergies (see Methods). As done with the procedure previously described, we tested all configurations that could be obtained by randomly selecting 1 to 1. Appendix figure 2, blue line), as well as four subsamples according to the setups described above (Appendix figure 2, light blue dots). The results show that, for the feature- based procedure, the accuracy increases as a function of the number of electrodes, reaching a peak with 1. SEM: 8. 1. 6 ± 2%); the mean accuracy across all the possible configurations with five channels is 7. The accuracy obtained with the setup adopted in our current paper was 7.
Ejaz et al. (2. 01. For the procedure described in Ejaz et al. In these data, the accuracy for the configuration of five channels adopted in our paper was 6. Ejaz et al. (5. 9. In summary, these results indicate that: 1) The extraction of features from the EMG signal obtained using the methodological approach adopted in our paper leads to a better discrimination of complex hand gestures. While the feature- based approach seems to benefit from EMG recordings with more channels, the low gain (5. The results from the electrode placement adopted by Ejaz et al.