Physiotherapist-Assisted Wrist Movement Protocol for EEG-Based Corticokinematic Coherence Assessment

This dataset accompanies the manuscript titled 'Physiotherapist-Assisted Wrist Movement Protocol for EEG-Based Corticokinematic Coherence Assessment' and includes synchronized EEG and movement recordings collected during a physiotherapist-assisted wrist movement task for corticokinematic coherence (CKC) analysis. The data are organized per participant and trial.ContentsEach participant's data are stored in files using the following naming convention:SXXX_ckc_HH_SS_BBWhere:SXXX: anonymized subject ID (random 3-digit code, e.g., S113, S479),HH: movement hand (bal = left hand, jobb = right hand),SS: session number (t1),BB: trial block number (01, 02).File TypesEach trial includes the following two files:Movement file (no extension, e.g., S113_ckc_bal_t1_01):Custom binary format containing synchronized hand acceleration data collected via a 3-axis accelerometer placed on the wrist.Movement File StructureEach entry contains:time: a 32-bit unsigned integer indicating the timestamp in milliseconds,x, y, z: 16-bit signed integers representing acceleration along the respective axes,trigger: an 8-bit unsigned integer used to mark event-related triggers for synchronization with the EEG data (e.g., movement onset).EEG files (.hed/.flo pair)EEG was recorded with a 64-channel Synamp RT system (Compumedics Neuroscan, Victoria, Australia) at 2000 Hz.Converted from the original Neuroscan CNT format into an open binary format..hed: plain-text header describing the dataset (channels, sampling rate, etc.). Each line starts with a keyword such as:Datatype: always float (4-byte floats),Number of timepoints: total samples,Starting timepoint: always 0,Sampling rate: 1000 Hz,Ch: channel labels in order of appearance in the .flo file.Other fields (e.g. Number of channels hint) can be ignored..flo: binary file with EEG samples stored as little-endian 4-byte floats in channel-major order:[Channel 1: sample 1 ... last sample] [Channel 2: sample 1 ... last sample] ...Example (Python, using numpy):import numpy as np, osfilename = "S113_ckc_bal_t1_01"# Read channel labels from .hedchannels = []with open(filename + ".hed", "r") as f: for line in f: if line.startswith("Ch:"): channels.append(line.split(":")[1].strip())n_channels = len(channels)# Determine number of samplesfilesize = os.path.getsize(filename + ".flo")n_samples = filesize // (4 * n_channels)# Load EEG dataraweeg = np.fromfile(filename + ".flo", dtype="f4")raweeg = raweeg.reshape(n_channels, n_samples)NotesData are organized by movement side (bal = left, jobb = right), trial (t1), and block repetition (01, 02).The EEG and movement files are time-locked to enable CKC computation.Stimuli were delivered manually by a trained physiotherapist with visual pacing using a screen-based metronome.UsageThese data can be used to replicate the analyses reported in the manuscript, or for methodological and clinical studies of proprioception using EEG-based CKC.Please cite the associated paper when using this dataset.