Impact of compressed sensing of motor cortical activity on spike train decoding in Brain Machine Interfaces

Decoding spike trains is an essential step to translate multiple single unit activity to useful control commands in cortically controlled Brain Machine Interface (BMI) systems. Extracting the spike trains of individual neurons from the recorded mixtures requires spike sorting, a computationally prohibitive step that precludes the development of fully implantable, small size and low power electronics. Previously, we reported on the ability to extract the critical information in these spike trains such as precise spike timing and firing rate of individual neurons using a compressed sensing strategy that overcomes the computational burden of the spike sorting step. Herein, we assess the decoding performance using this method and compare it to the case where classical spike sorting takes place prior to decoding. We use the local average of the sparsely represented data as discriminative features to “informally” detect and classify spikes in the data stream. We demonstrate that there is a substantial gain in performance assessed under different decoding strategies, while much less computations are needed compared to spike sorting in the traditional sense.