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

Abstract

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.

Quantitative evaluation of muscle synergy models: a single-trial task decoding approach

“…This conceptualization mirrors the one often used in single-trial neural literature to separate signal from noise and identify the most informative components of neural responses (Quian Quiroga and Panzeri, 2009). The advantage of this method is that it allows the user to focus on task-discriminant aspects of variability using an objective and useful scale in a user-defined “task space” rather than measuring variability on a scale related only to the amplitude of the EMG signals (Quian Quiroga and Panzeri, 2009; Tolambiya et al, 2011). …”

Comparisons of Prediction Models of Myofascial Pain Control after Dry Needling: A Prospective Study

“…The SVM model employs nonlinear mapping to transform the original training data into higher-dimensional data and searches for the linear optima that define a hyperplane within the new dimension [8]. With appropriate nonlinear mapping to a sufficiently high dimension, a decision boundary can separate data into two classes [8].…”

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Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review

“…Table 9 reports SVM features relative to EMG-based walking modes recognition papers. [157] authors used an SVM with Wigner kernel (the Wigner kernel is defined as ( , ) = |〈 , 〉| 2 , with <.,.> denoting the inner product) for the discrimination of five body pointing tasks with or without postural or focal constraints. A mean accuracy close to or higher than 80% was achieved in discriminating constrained from unconstrained movements.…”