Tracking times in temporal patterns embodied in intra-cortical data for controling neural prosthesis an animal simulation study

Brain-machines capture brain signals in order to restore communication and movement to disabled people who suffer from brain palsy or motor disorders. In brain regions, the ensemble firing of populations of neurons represents spatio-temporal patterns that are transformed into outgoing spatio-temporal patterns which encode complex cognitive task. This transformation is dynamic, non-stationary (time-varying) and highly nonlinear. Hence, modeling such complex biological patterns requires specific model structures to uncover the underlying physiological mechanisms and their influences on system behavior. In this study, a recent multi-electrode technology allows the record of the simultaneous neuron activities in behaving animals. Intra-cortical data are processed according to these steps: spike detection and sorting, than desired action extraction from the rate of the obtained signal. We focus on the following important questions about (i) the possibility of linking the brain signal time events with some time-delayed mapping tools; (ii) the use of some suitable inputs than others for the decoder; (iii) a consideration of separated data or a special representation founded on multi-dimensional statistics. This paper concentrates mostly on the analysis of parallel spike train when certain critical hypotheses are ignored by the data for the working method. We have made efforts to define explicitly whether the underlying hypotheses are actually achieved. In this paper, we propose an algorithm to define the embedded memory order of NARX recurrent neural networks to the hand trajectory tracking process. We also demonstrate that this algorithm can improve performance on inference tasks

A New Method for Time-Jerk Optimal Trajectory Planning Under Kino-dynamic Constraint of Robot Manipulators in Pick-and-Place Operations

A new method for time-jerk optimal planning under Kino-dynamic constraints of robot manipulators in pick-and-place operations is described in this paper. In order to ensure that the resulting trajectory is smooth enough, a cost function containing a term proportional to the integral of the squared jerk (defined as the derivative of the acceleration) along the trajectory is considered. Moreover, a second term, proportional to the total execution time, is added to the expression of the cost function. A Cubic Spline functions are then used to compose overall trajectory. This method makes it possible to deal with the kinematic constraints as well as the dynamic constraints imposed on the robot manipulator. The algorithm has been tested in simulation yielding good results

Neural spike sorting with a self-training semisupervised support vector machine.

International audienceBrain decoding would be a replacement for some nerve injured patients to communicate motor functions with a prosthesis device. Decoding algorithms translate ensemble of firing rates to the intended function. Firing rates for each individual neuron are obtained from labeling the detected spikes. This labeling process-also known as spike sorting-could be done from the range of fully automated to a heavily operator dependent manners. On the other hand we could use merits of both automation and operator's watch in a semi-supervised approach. In this study we explored the application of a self-training SVM classifier algorithm to label spikes with a small training dataset. Result shows the proved monotonically increasing convergence and consequently the ability of this algorithm to significantly reduce the operator's effort for continuous supervision. It provides in addition a significant improvement with respect to the previously used SVMs

Decoding Hand Trajectory from Primary Motor Cortex ECoG Using Time Delay Neural Network

International audienceBrain-machines - also termed neural prostheses, could potentially increase substantially the quality of life for people suffering from motor disorders or even brain palsy. In this paper we investigate the non-stationary continuous decoding problem associated to the rat's hand position. To this aim, intracortical data (also named ECoG for electrocorticogram) are processed in successive stages: spike detection, spike sorting, and intention extraction from the firing rate signal. The two important questions to answer in our experiment are (i) is it realistic to link time events from the primary motor cortex with some time-delay mapping tool and are some inputs more suitable for this mapping (ii) shall we consider separated channels or a special representation based on multidimensional statistics. We propose our own answers to these questions and demonstrate that a nonlinear representation might be appropriate in a number of situations