Decoding the Spike-Band Subthreshold Motor Cortical Activity

Abstract

Intracortical Brain-Computer Interfaces (iBCI) use single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP) to control neuroprosthetic devices. SUA and MUA are usually extracted from the bandpassed recording through amplitude thresholding, while subthreshold data are ignored. Here, we show that subthreshold data can actually be decoded to determine behavioral variables with test set accuracy of up to 100%. Although the utility of SUA, MUA and LFP for decoding behavioral variables has been explored previously, this study investigates the utility of spike-band subthreshold activity exclusively. We provide evidence suggesting that this activity can be used to keep decoding performance at acceptable levels even when SUA quality is reduced over time. To the best of our knowledge, the signals that we derive from the subthreshold activity may be the weakest neural signals that have ever been extracted from extracellular neural recordings, while still being decodable with test set accuracy of up to 100%. These results are relevant for the development of fully data-driven and automated methods for amplitude thresholding spike-band extracellular neural recordings in iBCIs containing thousands of electrodes.

Keywords:

• neural noise
• population activity
• spiking
• amplitude thresholds
• primary motor cortex

Acknowledgment

This paper was accepted on the eve of October 29^th, 2023, the centennial of the founding of the Republic of Türkiye. We therefore dedicate our work to the founders of the Republic, and to those who uphold the following quote of our first President: “Benim manevi mirasım, bilim ve akıldır” (“My moral legacy is science and reason”) Mustafa Kemal ATATÜRK (1881-193∞).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data available on request from the authors.

Additional information

Funding

This work was supported in part by the Scientific Research Project Fund of Sivas Cumhuriyet University under the project number SMYO-023.