An EEG dataset for handwriting imagery decoding of Chinese character strokes and Pinyin single vowels

TL;DR

This paper introduces the first open EEG dataset for decoding handwriting imagery, focusing on Chinese character strokes and Pinyin vowels to aid motor-impaired individuals.

Contribution

The paper presents the first open EEG dataset specifically for handwriting imagery decoding, supporting non-invasive BCI development.

Findings

The dataset includes 18,480 standardized EEG trials from 21 participants.

It features two tasks: Chinese character stroke imagery and Pinyin vowel imagery.

The dataset adheres to the BIDS standard and is suitable for BCI algorithm development.

Abstract

Non-invasive EEG-based brain-computer interfaces (BCI) for handwriting imagery can support the restoration of fine writing abilities in individuals with motor impairments. However, the development of high-performance decoding algorithms is constrained by scarce training datasets. To address this, we present the first open EEG dataset dedicated to handwriting imagery. The dataset comprises 32-channel EEG recordings (sampled at 1000 Hz) from 21 healthy participants across two sessions separated by at least 24 hours. A dual-paradigm design captures multidimensional neural features: a Chinese character stroke imagery task (five basic strokes, 200 trials per session) and a Pinyin single-vowel imagery task (six vowels, 240 trials per session). After rigorous quality screening, 18,480 standardized trials are provided, ensuring completeness, reliability, and adherence to the Brain Imaging Data Structure (BIDS) standard. This dataset enables the development and evaluation of algorithms for non-invasive BCI and supports research on restoring writing-based communication in individuals with motor impairments.

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