Too much information is no information: how machine learning and feature selection could help in understanding the motor control of pointing

© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The aim of this study was to develop the use of Machine Learning techniques as a means of multivariate analysis in studies of motor control. These studies generate a huge amount of data, the analysis of which continues to be largely univariate. We propose the use of machine learning classification and feature selection as a means of uncovering feature combinations that are altered between conditions. High dimensional electromyogram (EMG) vectors were generated as several arm and trunk muscles were recorded while subjects pointed at various angles above and below the gravity neutral horizontal plane. We used Linear Discriminant Analysis (LDA) to carry out binary classifications between the EMG vectors for pointing at a particular angle, vs. pointing at the gravity neutral direction. Classification success provided a composite index of muscular adjustments for various task constraints—in this case, pointing angles. In order to find the combination of features that were significantly altered between task conditions, we conducted a post classification feature selection i.e., investigated which combination of features had allowed for the classification. Feature selection was done by comparing the representations of each category created by LDA for the classification. In other words computing the difference between the representations of each class. We propose that this approach will help with comparing high dimensional EMG patterns in two ways; (i) quantifying the effects of the entire pattern rather than using single arbitrarily defined variables and (ii) identifying the parts of the patterns that convey the most information regarding the investigated effects.Peer reviewe

An Ensemble Analysis of Electromyographic Activity during Whole Body Pointing with the Use of Support Vector Machines

We explored the use of support vector machines (SVM) in order to analyze the ensemble activities of 24 postural and focal muscles recorded during a whole body pointing task. Because of the large number of variables involved in motor control studies, such multivariate methods have much to offer over the standard univariate techniques that are currently employed in the field to detect modifications. The SVM was used to uncover the principle differences underlying several variations of the task. Five variants of the task were used. An unconstrained reaching, two constrained at the focal level and two at the postural level. Using the electromyographic (EMG) data, the SVM proved capable of distinguishing all the unconstrained from the constrained conditions with a success of approximately 80% or above. In all cases, including those with focal constraints, the collective postural muscle EMGs were as good as or better than those from focal muscles for discriminating between conditions. This was unexpected especially in the case with focal constraints. In trying to rank the importance of particular features of the postural EMGs we found the maximum amplitude rather than the moment at which it occurred to be more discriminative. A classification using the muscles one at a time permitted us to identify some of the postural muscles that are significantly altered between conditions. In this case, the use of a multivariate method also permitted the use of the entire muscle EMG waveform rather than the difficult process of defining and extracting any particular variable. The best accuracy was obtained from muscles of the leg rather than from the trunk. By identifying the features that are important in discrimination, the use of the SVM permitted us to identify some of the features that are adapted when constraints are placed on a complex motor task

EMG recordings.

<p>Traces of the 24 muscles recorded from an individual during a whole body pointing task. Muscle abbreviations are explained in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020732#s2" target="_blank">Methods</a> section. Recordings from the Bic are missing due to difficulties with the electrode for this individual. The EMG signals presented were recorded at a sampling frequency of 1000 Hz followed by rectification. The first trace in each column represents finger velocity. Finger movement onset is indicated by <i>t_o</i>, the instant of its maximum velocity by <i>t_pv</i> and the instant of finger movement termination by <i>t_f</i>.</p

Individual postural muscle classification accuracies.

<p>Mean classification accuracies obtained when using individual postural muscle EMGs for a B vs S classification. Muscles are named using abbreviated forms. Full names may be obtained in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020732#s2" target="_blank">Methods</a> section.</p

Stick diagrams of the task performed under basic condition (B), equilibrium constraints (K, R) and spatial constraints (S, C).

<p>B: Basic condition. K: Knee-extended condition. R: Reduced base of support condition. S: Imposed straight finger trajectory condition. C: Imposed semicircular trajectory condition. The dark gray and light gray dotted traces depict the CoM and the finger trajectories in the sagittal plane, respectively. The inset box defines the body parts in the stick diagram (Sk, shank; Th, thigh; Pe, pelvis; Tr, trunk; He, head; Hu, humerus; Fo, forearm; Ha, hand).</p

Classification accuracies using the <i>maximum amplitude vector</i> and the <i>maximum time point vector</i>.

<p>Mean values of correct responses obtained for classification between constrained and unconstrained conditions. Values are reported as percentage above chance levels. The asterisk marks cases for which the categorization success using the <i>maximum amplitude vector</i> was significantly higher than with the <i>maximum time point vector</i>. Statitical tests were carried out using a Friedmann test followed by the Wilcoxon tests with a Bonferroni correction for multiple comparisons.</p

Optimal separating hyper-plane in SVMs for a linearly non-separable case.

<p>Black and white squares refer to the classes ‘+1’ and ‘−1’, respectively. Support vectors are indicated by an extra square.</p