DOI: 10.6084/m9.figshare.29987758.v2
title: <b>EEG dataset for multi-class Chinese character stroke and pinyin vowel handwriting imagery (16 subjects, CCS-HI & SV-HI)</b>

description: Version HistoryVersion 2.0 (October 15, 2025)Event label correction: Corrected a scaling inconsistency in the event tags within the file sub-13_ses-02_task-CCSHI_eeg.bdf. The event codes now correctly align with the task paradigm's coding logic.Metadata update: Corrected the handedness metadata for participant sub-06 in participants. tsv from "L" to "R", to address a typographical error in the original metadata.Quality assurance: The entire dataset underwent a full BIDS validation following these modifications to confirm compliance with the BIDS standard.No changes to the associated study's results or conclusions are necessitated by these updates.1. Research ContextThis dataset addresses a critical gap in Brain-Computer Interface (BCI) research: most existing resources focus on English-language tasks or simple motor imagery, while Chinese character strokes and Pinyin monophthong handwriting remain understudied. By providing data for two complementary handwriting imagery tasks—Chinese Character Stroke Handwriting Imagery (CCS-HI): Imagining writing 5 basic Chinese character strokes( 一,丨,丿,㇏,ㄥ) ;Pinyin Single Vowel Handwriting Imagery (SV-HI): Imagining writing 6 Pinyin monophthongs (a, o, e, i, u, ü)—we enable investigations into:The neural mechanisms of handwriting imagery for Chinese strokes vs. Pinyin characters;Cross-session generalization (a critical challenge for real-world BCI deployment).2. Data OverviewSubjects: 16 healthy adults (3 females, age 22–32, right-handed, no neurological disorders). Note: In Version 2.0, handedness for one subject was corrected from “L” to “R” to reflect accurate right-handedness; all participants remain confirmed as right-handed.Tasks:CCS-HI: Imagine writing 5 Chinese strokes (一，丨，丿，㇏, ㄥ；200 trials/session).SV-HI: Imagine writing 6 Pinyin monophthongs (a, o, e, i, u, ü; 240 trials/session).Sessions: 2 independent sessions (≥24 hours apart).Session 1: Used for training and 5-fold cross-validation.Session 2: Held out as an independent test set for cross-session evaluation.3. Experimental Design3.1 Pre-Experiment PreparationPrior to data collection, all subjects were fully informed of the experimental protocol (including task requirements and response methods) and shown a demonstration video of handwriting imagery tasks to ensure understanding. Written informed consent was obtained from all participants.3.2 EEG AcquisitionDevice: Neuracle NeuSenW amplifier with a 32-channel Ag/AgCl electrode cap.Electrode Layout: Strictly following the international 10-10 system. Detailed 3D coordinates and nomenclature of each electrode are provided in the accompanying electrodes.tsv file.Sampling Rate: 1000 Hz (downsampled to 250 Hz during preprocessing).Electrode Impedance: Maintained below 10 kΩ for all channels during recording.Calibration: All equipment was professionally calibrated before the experiment to ensure data quality and reproducibility.3.3 Task Paradigm3.2.1 General Trial Structure (CCS-HI & SV-HI)Each single trial included four sequential phases, with the total duration varying slightly by task:1. Fixation + Auditory Cue: 2 secondsA white fixation cross was displayed on a black screen to stabilize subjects’ attention;An auditory beep (500 Hz, 100 ms) was synchronized with the start of the cross, serving as a trial initiation signal.2. Task Cue Phase: Duration differs by task (key distinction)2.1 For CCS-HI: 2.8 secondsA dynamic animation of the target Chinese character stroke (e.g., 一，丨) was played, demonstrating the stroke’s writing trajectory.2.2 For SV-HI: 3.2 secondsA dynamic animation of the target Pinyin monophthong (e.g., a, o) was played; the extended duration was designed to accommodate the relatively higher complexity of visualizing Pinyin character shapes.3. Handwriting Imagery Phase: 4 secondsSubjects were instructed to imagine writing the target stroke (CCS-HI) or Pinyin monophthong (SV-HI) following the trajectory shown in the task cue, without any actual limb movement.4. Rest Interval: 3 secondsA blank black screen was presented to allow subjects to recover and reset attention for the next trial.3.2.2 Trial Duration SummaryTotal duration per trial (CCS-HI): 2 s (fixation) + 2.8 s (cue) + 4 s (imagery) + 3 s (rest) = 11.8 seconds.Total duration per trial (SV-HI): 2 s (fixation) + 3.2 s (cue) + 4 s (imagery) + 3 s (rest) = 12.2 seconds.4. Preprocessing & Trial IntegrityThe publicly released dataset contains raw EEG data (no preprocessing); preprocessing (via MNE-Python, code in code folder) was only conducted for model training/testing: 1–40 Hz Butterworth bandpass filtering + 50 Hz notch filtering for noise reduction, manual bad channel labeling (EEGLAB) and spherical spline interpolation (per BIDS _channels.tsv), downsampling from 1000 Hz to 250 Hz, z-score normalization per trial, and epoch extraction of the 0–4 s imagery period (for both tasks). Trial integrity checks confirmed most sessions met standards (200 trials/session for CCS-HI, 240 for SV-HI), with two exceptions: Subject 06 (SV-HI, Ses-2: 225 trials, trigger error) and Subject 13 (CCS-HI, Ses-2: 162 trials, fatigue). All retained trials passed quality checks, and missing trials were evenly distributed across categories (max deviation ≤2 trials, &lt;5% threshold), so no class balancing was needed.<br>5. File Structure (BIDS-Compliant)Following corrections to event labels and participant metadata in Version 2.0, the dataset underwent full BIDS compliance re-verification to ensure data structure and metadata consistency.The dataset follows the BIDS standard: the root directory stores core metadata (e.g., <code>dataset_description.json</code> for dataset info, <code>participants.tsv</code> for demographics, <code>electrodes.tsv</code> for 32-channel 10-10 system coordinates, task-specific <code>_events.json</code>), 16 subject folders (e.g., <code>sub-01</code>) each contain <code>ses-01/ses-02</code> with <code>eeg/</code> subfolders (by CCS-HI/SV-HI tasks) holding <code>_channels.tsv</code> (bad channels), <code>_eeg.bdf</code> (raw Neuracle EEG), <code>_eeg.json</code> (acquisition params), <code>_events.tsv</code> (task alignment); supplementary folders include <code>stimuli</code> (visual cues) and <code>code</code> (preprocessing/model scripts with docs).6. Usage & Citation6.1 Data Access & ExtractionThe HI-EEG dataset (CCS-HI/SV-HI tasks) is available via Figshare: https://doi.org/10.6084/m9.figshare.29987758(raw data access). To ensure successful decompression of the split-volume compressed files, follow these guidelines:File Composition: The dataset is split into 4 interlinked files (all required for full extraction):1. Main archive: `CCS-SV-HI-EEG_BIDS.zip`2. Split volumes: `CCS-SV-HI-EEG_BIDS.z01`, `CCS-SV-HI-EEG_BIDS.z02`, `CCS-SV-HI-EEG_BIDS.z03`Pre-Extraction Requirement: Download all 4 files and place them in the **same directory** (do not rename files or move them to subfolders).Tool & Operation:1. Use split-volume compatible software (RECOMMENDED: 7-Zip, open-source & free: https://www.7-zip.org/; avoid default system extractors like Windows Explorer/macOS Archive Utility).2. Double-click the main `.zip` file (`CCS-SV-HI-EEG_BIDS.zip`) — the software will automatically merge `.z01`–`.z03` into the full BIDS folder (matching the structure described in Section 5).Critical Note: Extraction will fail if any split file is missing; the decompressed folder retains the BIDS structure (no additional organization needed).<br>6.2 Trial Integrity & Usage DetailsNote two trial integrity details: sub-06 (SV-HI, ses-02) retains 225 trials (15 missing, trigger sync error) and sub-13 (CCS-HI, ses-02) retains 162 trials (38 missing, subject fatigue)—missing trials are evenly distributed across categories (max deviation ≤2 trials, &lt;5% imbalance, no class balancing needed).<br>For data loading/analysis, use EEGLAB (MATLAB) or MNE-Python 1.7.0 (Python); for decoding, use scikit-learn 1.6.1 or PyTorch 2.0.0+cu117 (all verified versions). Related code is in the repository’s code directory; refer to the README in code for environment configuration and script workflow.<br>6.3 CitationCite the associated publication (DOI to be added) when using this dataset.