On-chip decoder and method for decoding neural activity, brain implant comprising the same and prosthetic system comprising the brain implant

The present invention concerns an on-chip decoder (ocD), a brain implant, a prosthetic system and a method for decoding the task-related brain activity from multi-unit recordings of groups of neurons in a brain area and convert it into digital data usable by a classifier generating data labels corresponding to task-related information processed by said brain area, wherein said decoder uses multi-unit threshold crossing rates, called neural activity, subdivided into channels and time slots representing features of neural activity, in which it measures both the total magnitude and the uniformity of between-classes distances across all features to extract the most salient features and thereby generate a neural code (DNC) representing the highest class saliency values and reducing the neuronal state space into a low-dimensional DNC subspace, the parameters of the On-chip decoder (ocD) being computed offline during training and stored in an on-chip memory for online decoding.