Spatial Sampling and Temporal Dynamics Shape Motor Decoding from Broadband Intracortical Signals

Objective. Neural decoding performance depends strongly on neural signal representation; however, how the informative components of broadband intracortical activity vary with electrode configuration and decoding strategy remains unclear. We systematically compared the neural features spanning multiple temporal scales to identify the factors underlying robust motor decoding. Approach. Neural recordings were obtained from the dorsal premotor cortex, primary motor cortex, and dorsolateral prefrontal cortex of rhesus monkeys performing delayed-reaching tasks toward static or moving targets. Signals were recorded using 64-channel linear S-probes or 96-channel planar Utah arrays. We compared spiking-band power (SBP), local motor potential (LMP), and local field potential (LFP) power across multiple frequency bands using linear and nonlinear decoders. We further characterized the spectral and temporal components contributing to LMP decoding and evaluated the channel redundancy. Main results. The decoding performance of the SBP and high-frequency LFP features scaled with the number of recorded single units, whereas the LMP performance was more strongly influenced by the electrode geometry. LMP achieved a higher decoding performance with planar Utah arrays than with linear S-probes, accompanied by lower inter-electrode correlations and greater variability in preferred directions. Spectral analysis showed that LMP decoding was dominated by low-frequency components overlapping the delta band and movement-related cortical potentials, with an optimal decoding range of approximately 0.1-8 Hz. Nonlinear temporal decoders, particularly the convolutional neural network and gated recurrent unit models, further improved LMP decoding. Significance. These findings demonstrate that the neural features most informative for motor decoding depend on both the spatial sampling configuration and the temporal structure of the recorded activity. The results highlight the importance of low-frequency population dynamics and provide principles for optimizing neural-signal representations and reducing redundant neural data for scalable and wireless brain-computer interfaces.