Cortico-subcortical β burst dynamics underlying movement cancellation in humans

These local field potential (LFP) recordings were made on a Tucker-Davis technologies (Alachua, FL) system, using a RA16PA 16-Channel Medusa pre-amplifier and a RA16LI head-stage. The sampling rate for recording was 24Hz or 2Hz, with a low-pass filter of 7.5 kHz on the hardware side. Stimulus onsets were marked in the recording using a TTL pulse from a USB Data Acquisition Device (USB-1208FS, Measurement Computing, Norton, MA) triggered by the stimulus presentation laptop. Data were collected from two four-, six-, or eight-contact strip electrodes placed in the subgaleal space over SMC (Ad-Tech, Oak Creek, WI; 10 mm spacing center-to-center, 3 mm exposed contact diameter) and DBS leads (3387, Medtronic, Inc, Minneapolis, MN) placed in either the thalamus or subthalamic nucleus. Raw data are stored in the folder named "data". Within this folder are two subfolders, "main" (containing the datasets from the main behavioral task) and "localizers" (containing the datasets from the go-only localization block used to confirm placement of subgaleal electrodes over hand motor regions). LFP data were preprocessed and analyzed using custom MATLAB scripts included with this dataset. Electrical line noise from the operating room environment was filtered from the data using EEGLAB’s (Delorme and Makeig, 2004) cleanline function after which the recordings were down-sampled to 1000Hz for analysis. Then, the recordings were visually inspected for any artifacts. Any 1s segment of the recording containing an artifact was removed from the data, yielding the preprocessed sets stored in the folder named "pp". β burst detection was performed using the same procedure as in Wessel (2020) and Shin et al. (2017). Data from each bipolar electrode array were convolved with a complex Morlet wavelet. The absolute value of the resulting complex data was squared to yield time-frequency power estimates. The resulting time-frequency data were epoched around events of interest (go and stop signals) with a window of 500ms before stimulus onset to 1000ms after stimulus onset. β bursts were classified by identifying local maxima in the trial-by-trial time-frequency data that exceeded six times the median of the time-frequency power for that specific array across the recording and that lasted at least two β cycles. Indices of these beta bursts in the recordings are stored in the data structures included in the "beta" folder. For more information on data collection, preprocessing, and analysis, please see the associated manuscript.