Estimation of two-digit grip type and grip force level by frequency decoding of motor cortex activity for a BMI application

Abstract

This study focuses on the estimation of kinematic and kinetic information during two-digit grasping using frequency decoding of motor cortex spike trains for brain machine interface applications. Neural data were recorded by a 100-microelectrode array implanted in the motor cortex of one monkey performing instructed reach-grasp-and-pull movements. Decoding of neural data was performed by two different algorithms: i) through Artificial Neural Networks (ANN) consisting of a multi layer perceptron (MLP), and ii) by a Support Vector Machine (SVM) with linear kernel function. Decoding aimed at classifying the upcoming grip type (precision grip vs. side grip) as well as the required grip force (low vs. high). We then used the decoded information to reproduce the monkey's movement on a robotic platform consisting of a two-finger, eleven degrees of freedom (DoF) robotic hand carried by a six DoF robotic arm. The results show that 1) in terms of performance there was no significant difference between ANN and SVM prediction. Both algorithms can be used for frequency decoding of multiple motor cortex spike trains: good performance was found for grip type prediction, less so for grip force. 2) For both algorithms the prediction error was significantly dependent on the position of the input time window associated to different stages of the instructed grasp movement. 3) The lower performance of grasp force prediction was improved by optimizing the neuronal population size presented to the ANN input layer on the basis of information redundancy.