Elsevier

Neuroscience

Volume 188, 11 August 2011, Pages 80-94
Neuroscience

Cognitive, Behavioral, and Systems Neuroscience
Research Paper
Decoding the activity of grasping neurons recorded from the ventral premotor area F5 of the macaque monkey

https://doi.org/10.1016/j.neuroscience.2011.04.062Get rights and content

Abstract

Many neurons in the monkey ventral premotor area F5 discharge selectively when the monkey grasps an object with a specific grip. Of these, the motor neurons are active only during grasping execution, whereas the visuomotor neurons also respond to object presentation. Here we assessed whether the activity of 90 task-related F5 neurons recorded from two macaque monkeys during the performance of a visually-guided grasping task can be used as input to pattern recognition algorithms aiming to decode different grips. The features exploited for the decoding were the mean firing rate and the mean interspike interval calculated over different time spans of the movement period (all neurons) or of the object presentation period (visuomotor neurons). A support vector machine (SVM) algorithm was applied to the neural activity recorded while the monkey grasped two sets of objects. The original set contained three objects that were grasped with different hand shapes, plus three others that were grasped with the same grip, whereas the six objects of the special set were grasped with six distinctive hand configurations. The algorithm predicted with accuracy greater than 95% all the distinct grips used to grasp the objects. The classification rate obtained using the first 25% of the movement period was 90%, whereas it was nearly perfect using the entire period. At least 16 neurons were needed for accurate performance, with a progressive increase in accuracy as more neurons were included. Classification errors revealed by confusion matrices were found to reflect similarities of hand grips used to grasp the objects. The use of visuomotor neurons' responses to object presentation yielded grip classification accuracy similar to that obtained from actual grasping execution. We suggest that F5 grasping-related activity might be used by neural prostheses to tailor hand shape to the specific object to be grasped even before movement onset.

Highlights

▶F5 cortical activity has been fed to recognition algorithms for grip decoding. ▶Distinctive hand postures can be predicted by algorithms with great accuracy. ▶The correct grip can be predicted even before movement onset. ▶F5 neuronal discharge could be used in the control of a prosthetic hand.

Section snippets

Experimental procedures

Properties of the neurons analyzed in this paper have been reported in a previous study (Raos et al., 2006). Single unit activity was recorded from area F5 in the posterior bank of the inferior limb of the arcuate sulcus in three hemispheres (contralateral to the moving forelimb) of two awake monkeys (Macaca nemestrina). The behavioral apparatus and paradigm are summarized in the next section (for more details see (Raos et al., 2006)). All experimental protocols were approved by the

Results

Different algorithms have been tested for the classification of the features extracted from the signals recorded from F5 grasping-related neurons at time intervals spanning from 25% to 100% of movement or object presentation periods, using different sets of objects/grips (original and special, see Fig. 3). In particular, five classifiers have been tested to discriminate either six grips as if each object required a different grip, or clusters of grips (from three to five) according to the

Discussion

The results of the present study show that F5 grasping-related neurons represent a reliable source of information for the implementation of decoding algorithms that could eventually be used for the control of artificial hand grasping. In most cases, few neurons and short window lengths for the extraction of the features were sufficient to achieve a good prediction. The classifier could predict the six grips for the special set of objects and the four grips for the original set of objects.

Acknowledgments

The work described here was partly supported by the EU within the NEUROBOTICS Integrated Project (IST-FET Project 2003-001917 “The fusion of NEUROscience and roBOTICS”).

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