Decoding of Distinct Movement Phases in a Reach-to-Grasp Task Performed by a Non-Human Primate Using a Wavelet Scattering Framework

When it comes to helping patients with movement-related disorders (e.g. spinal cord injuries, stroke), neural engineering applications such as brain-machine (computer) interfaces (BMIs) hold great promise. One of the key ingredients to building a successful BMIs lies in the ability to read specific user instructions from their brain. Exploiting this information using neural signals is crucial to send commands to an output device (such as a robotic arm) to perform the desired action. The issue to understand how we can read the desired action from the neural activity is called neural decoding. Nowadays, implementing sophisticated decoding algorithms for proper detection and translation of neural signals into suitable instructions is a central topic for neural engineering applications, for example, the development of fully functional BMIs. Here, we implemented an advanced decoding algorithm to decode distinct phases of movement in an instructed-delay reach-to-grasp task performed by a non-human primate. Specifically, we were interested to decode four distinct epochs, i.e. Free, Obj-Vis, Delay, Grasp, in two different conditions (light and dark conditions). Briefly, the Free period is related to a rest condition, Obj-Vis and Delay to movement preparation and Grasp to movement execution. In the light condition, the animal can see the object to grasp whereas in the dark condition the object is visible only for a short period. Neural signals were acquired using a multielectrode array implanted in the primary motor cortex (M1) of a Macaca fascicularis. In particular, as a signal for our decoding task, we used the Local Field Potential (LFP) that represents the low frequency (=