Direction and speed tuning of motor-cortex multi-unit activity and local field potentials during reaching movements

Primary motor-cortex multi-unit activity (MUA) and local-field potentials (LFPs) have both been suggested as potential control signals for brain-computer interfaces (BCIs) aimed at movement restoration. Some studies report that LFP-based decoding is comparable to spiking-based decoding, while others offer contradicting evidence. Differences in experimental paradigms, tuning models and decoding techniques make it hard to directly compare these results. Here, we use regression and mutual information analyses to study how MUA and LFP encode various kinematic parameters during reaching movements. We find that in addition to previously reported directional tuning, MUA also contains prominent speed tuning. LFP activity in low-frequency bands (15-40Hz, LFP_L) is primarily speed tuned, and contains more speed information than both high-frequency LFP (100-300Hz, LFP_H) and MUA. LFP_H contains more directional information compared to LFP_L, but less information when compared with MUA. Our results suggest that a velocity and speed encoding model is most appropriate for both MUA and LFP_H, whereas a speed only encoding model is adequate for LFP_L.