Neural Feature Extraction for iBCI Performance

In all participants, broad-band neural recordings were sampled at 30 kHz and pre-processed in Simulink using the xPC real-time operating system (The Mathworks Inc., Natick, MA, US; RRID: SCR_014744). From each pre-processed channel, extraction of two neural features from non-overlapping 20 millisecond time bins was performed in real time, as illustrated in Fig. 1A. These included 192 unsorted threshold crossing (TC) and 192 spike band power (SBP) features. Here, we evaluated the neural space by characterizing TC and SBP features as opposed to sorted single units, in order for our results to be directly applicable to iBCI systems, and because iBCI performance has been shown to be comparable when derived from thresholded data as opposed to spike-sorted data^{89 (link)–92 (link)}. Supplementary Figs. S8–S11 illustrate a visual comparison of neural activity from threshold crossings and sorted single units extracted from identical channels. Unless otherwise stated, all offline analyses were performed using MATLAB software (The Mathworks Inc., Natick, MA, US; RRID: SCR_001622). Neural recordings, multiunit feature extraction methods, and single unit sorting methods for this study are described in more detail within the Supplementary Information.