{
  "id": 12225545,
  "title": "Spatial Sampling and Temporal Dynamics Shape Motor Decoding from Broadband Intracortical Signals",
  "url": "https://urgent.news/2026/10/05/spatial-sampling-and-temporal-dynamics-shape-motor-decoding-from",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-05T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.29.755257v1?rss=1"
  },
  "original_language": "en",
  "account": "Neural decoding effectiveness hinges on the way neural signals are represented, yet the impact of electrode setup and decoding techniques on broadband intracortical activity is not well understood. To address this, researchers examined various temporal and spatial aspects of neural recordings from the dorsal premotor cortex, primary motor cortex, and dorsolateral prefrontal cortex of rhesus monkeys engaging in delayed-reaching tasks. The signals were captured using 64-channel linear S-probes or 96-channel planar Utah arrays, and both linear and nonlinear decoders were employed to analyze spiking-band power, local motor potential, and local field potential power across a range of frequencies.\n\nThe study revealed that decoding performance for spiking-band power and high-frequency local field potential features increased with the number of recorded single units, while local motor potential performance was more heavily influenced by the electrode configuration. Planar Utah arrays outperformed linear S-probes in local motor potential decoding, reducing inter-electrode correlations and increasing variability in preferred directions. When analyzing the spectral components, it was found that low-frequency components in the delta band, overlapping with movement-related cortical potentials, were crucial for accurate decoding, with the optimal range around 0.1-8 Hz. Nonlinear temporal decoders, such as convolutional neural network and gated recurrent unit models, further enhanced local motor potential decoding.\n\nThese findings underscore the significance of both spatial sampling and temporal dynamics in determining the most informative neural features for motor decoding. The results provide insights into optimizing neural signal representations and minimizing redundant data, paving the way for improved scalable and wireless brain-computer interfaces.",
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}