Summary of the Data and Experiment General Overview The data provided come from an attempted speech experiment involving a participant (T16) in the BrainGate2 clinical trial. The dataset includes neural recordings from four microelectrode arrays implanted in the left precentral gyrus. The primary focus is on the array located in the ventral precentral gyrus (6v), which is associated with speech-related activity. Data Description The neural data consists of two main streams: Log Local Field Potential (LFP) Power: Description: Extracellular neural data from the lower frequency bands. Sampling Rate: Preprocessed to 2 kHz. Filtering: Bandpass filtered with a 150-450 Hz band. Scale: Log-scale Bin size: 20ms Summed Shape: 128457x64 (time by channels). Binned Threshold Crossings: Description: Extracellularly collected neural spiking activity related to attempted speech. Threshold: -4.5 RMS Bin size: 20ms Summed Shape: 128457x64 (time by channels) The included data are within a single mat file with the keys `trial_info_df`, `lfp_matrix`, `spikes_matrix`, and `timevector`. Trial Information The `trial_info_df` key contains information about each trial in the form of a pandas DataFrame with the following columns: block_num cue start_time go_cue_time end_time Participant and Experiment Information Participant T16: Right-handed, 52-year-old woman with tetraplegia and dysarthria due to a pontine stroke. Implantation: Four 64-channel intracortical microelectrode arrays in the left precentral gyrus. Recording Day: Data collected 69 days post-implantation. Recording Platform: Backend for Realtime Asynchronous Neural Decoding (BRAND) platform. Spiking Data Extraction: Linear regression referencing, bandpass filtering (250-5000 Hz), and threshold crossings identification (-4.5 RMS threshold). LFP power Extraction: Band pass filtering (150 - 450 Hz), and notch filtering at harmonics of 60 Hz. Experimental Task Task: Cued speech task where Participant T16 vocalized words presented on a screen. Word Bank: Compiled from a 50-word vocabulary by Moses et al. Trial Structure: A red square appeared below a word. After 1500 ms, the square turned green, cueing the participant to vocalize the word. The trial ended after the participant finished speaking, with a 1000 ms interval before the next trial. Loading and Handling the Data The data can be loaded into Python using `scipy.io.loadmat`: Loading the Data import scipy.ioimport pandas as pd # Load the .mat filedata = scipy.io.loadmat('path_to_mat_file.mat') # Extracting the trial informationtrial_info_df = pd.DataFrame(data['trial_info_df'])trial_info_df.columns = ['block_num', 'cue', 'start_time', 'go_cue_time', 'end_time'] # Extracting neural data matriceslfp_matrix = data['lfp_matrix']spikes_matrix = data['spikes_matrix'] # Extracting time vectortimevector = data['timevector'] Wrapping Trial Information in a DataFrame trial_info_df = pd.DataFrame(data['trial_info_df'], columns=['block_num', 'cue', 'start_time', 'go_cue_time', 'end_time']) Exploring the Data Log Local Field Potential (LFP) Power: `lfp_matrix` Threshold Crossings (Spikes): `spikes_matrix` Time Vector: `timevector` Support This work was supported by NIH-NINDS/OD DP2NS127291 (CP), NIH-NICHD F32HD112173 (SRN), NIH K08NS060223 (MS), R01NS112942 (MS), and RF1NS125026 (MS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, or the Department of Veterans Affairs, or the United States Government. *Caution: Investigational Device. Limited by federal law to investigational use.