Neural decoding of movement targets by unsorted spike trains

Decoding movement targets from neural activity in motor cortex using invasive brain-computer interface (BCI) has potential application to help disabled patients. Most works employed spike sorting to obtain the single units (SUs) for decoding from the extracellular electrode recordings. However, spike sorting is difficult, computational demanding, and is often limited by the spike waveform variability especially in low SNR and high neuronal density conditions. To address these issues, we proposed a decoding method using unsorted spike trains from recording electrodes based on the maximal likelihood (ML) estimation approach. An experiment was performed to test neuronal data recorded from a rhesus monkey performing the center-out movement task of eight targets. The results showed that the proposed method yielded average correct decoding rate of 98.5% compared to the SU based method that yielded correct decoding rate of 96.3%. The results also showed that the proposed method yielded improved computational efficiency. Thus the proposed method showed potential for real time BCI applications with large scale of neuronal recordings.