Decoding the Grasping Intention from Electromyography during Reaching Motions

Background: Active upper-limb prostheses are used to restore important hand functionalities, such as grasping. In conventional approaches, a pattern recognition system is trained over a number of static grasping gestures. However, training a classifier in a static position results in lower classification accuracy when performing dynamic motions, such as reach-to-grasp. We propose an electromyography-based learning approach that decodes the grasping intention during the reaching motion, leading to a faster and more natural response of the prosthesis. Methods and Results: Eight able-bodied subjects and four individuals with transradial amputation gave informed consent and participated in our study. All the subjects performed reach-to-grasp motions for five grasp types, while the elecromyographic (EMG) activity and the extension of the arm were recorded. We separated the reach-to-grasp motion into three phases, with respect to the extension of the arm. A multivariate analysis of variance (MANOVA) on the muscular activity revealed significant differences among the motion phases. Additionally, we examined the classification performance on these phases. We compared the performance of three different pattern recognition methods; Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) with linear and non-linear kernels, and an Echo State Network (ESN) approach. Our off-line analysis shows that it is possible to have high classification performance above 80% before the end of the motion when with three-grasp types. An on-line evaluation with an upper-limb prosthesis shows that the inclusion of the reaching motion in the training of the classifier importantly improves classification accuracy and enables the detection of grasp intention early in the reaching motion. Conclusions: This method offers a more natural and intuitive control of prosthetic devices, as it will enable controlling grasp closure in synergy with the reaching motion. This work contributes to the decrease of delays between the user’s intention and the device response and improves the coordination of the device with the motion of the arm.

Improving the arm-hand coordination in neuroprosthetics control with prior information from muscle activity

Iason Batzianoulis

Humans use their hands mainly for grasping and manipulating objects, performing simple and dexterous tasks. The loss of a hand may significantly affect one's working status and independence in daily life. A restoration of the grasping ability is important to improve the quality of the daily life of the patients with motion disorders. Although neuroprosthetic devices restore partially the lost functionality, the user acceptance is low, possibly due to the artificial and unnatural operation of the devices. This thesis addresses this problem in reach-to-grasp motions with the development of shared control approaches that enable a seamless and more natural operation of hand prosthesis. In the first part, we focus on the identification of the grasping intention during the reach-to-grasp motion with able-bodied individuals. We propose an Electromyographic (EMG)-based learning approach that decodes the grasping intention at an early stage of reach-to-grasp motion, i.e. before the final grasp/hand pre-shape takes place. In this approach, the utilization of Echo State Networks encloses efficiently the dynamics of the muscle activation enabling a fast identification of the grasp type in real-time. We also examine the impact of different object distance and speed on the detection time and accuracy of the classifier. Although the distance from the object has no significant effect, fast motions influence significantly the performance. In the second part, we evaluate and extend our approach on four real end-users, i.e. individuals with below the elbow amputation. For addressing the variability of the EMG signals, we separate the reach-to-grasp motion into three phases, with respect to the arm extension. A multivariate analysis of variance on the muscle activity reveals significant differences among the motion phases. Additionally, we examine the classification performance on these phases and compare the performance of different pattern recognition methods. An on-line evaluation with an upper-limb prosthesis shows that the inclusion of the reaching motion in the training of the classifier improves importantly classification accuracy. In the last part of the thesis, we explore further the concept of motion phases on the EMG signals and its potentials on addressing the variability of the signals. We model the dynamic muscle contractions of each class with Gaussian distributions over the different phases of the overall motion. We extend our previous analysis providing insights on the LDA projection and quantifying the similarity of the distributions of the classes (i.e grasp types) with the Hellinger distance. We notice larger values of the Helinger distance and, thus, smaller overlaps among the classes with the segmentation to motion phases. A Linear Discriminant Analysis classifier with phase segmentation affects positively the classification accuracy.

EPFL2019

Evolutionary Selection of Features for Neural Sleep/Wake Discrimination

Peter Dürr, Dario Floreano, Jérémie Guignard, Walter Karlen, Claudio Mattiussi

In biomedical signal analysis, artificial neural networks are often used for pattern classification because of their capability for nonlinear class separation and the possibility to efficiently implement them on a microcontroller. Typically, the network topology is designed by hand, and a gradient-based search algorithm is used to find a set of suitable parameters for the given classification task. In many cases, however, the choice of the network architecture is a critical and difficult task. For example, hand-designed networks often require more computational resources than necessary because they rely on input features that provide no information or are redundant. In the case of mobile applications, where computational resources and energy are limited, this is especially detrimental. Neuroevolutionary methods which allow for the automatic synthesis of network topology and parameters offer a solution to these problems. In this paper, we use analog genetic encoding (AGE) for the evolutionary synthesis of a neural classifier for a mobile sleep/wake discrimination system. The comparison with a hand-designed classifier trained with back propagation shows that the evolved neural classifiers display similar performance to the hand-designed networks, but using a greatly reduced set of inputs, thus reducing computation time and improving the energy efficiency of the mobile system.

Hindawi Publishing Corporation2009

Recognition of Anticipatory Behavior from Human EEG

Ricardo Andres Chavarriaga Lozano, Gangadhar Garipelli

Anticipation increases the efficiency of a daily task by partial advance activation of neural substrates involved in it. Single trial recognition of this activation can be exploited for a novel anticipation based Brain Computer Interface (BCI). In the current work we compare different methods for the recognition of Electroencephalogram (EEG) correlates of this activation on single trials as a first step towards building such a BCI. To do so, we recorded EEG from 9 subjects performing a classical Contingent Negative Variation (CNV) paradigm (usually reported for studying anticipatory behavior in neurophysiological experiments) with GO and NOGO conditions. We first compare classification accuracies with features such as Least Square fitting Line (LSFL) parameters and Least Square Fitting Polynomial (LSFP) coefficients using a Quadratic Discriminant Analysis (QDA) classifier. We then test the best features with complex classifiers such as Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs).

2008

Improving the arm-hand coordination in neuroprosthetics control with prior information from muscle activity

Iason Batzianoulis

EPFL2019