用脑电+手部运动数据解码手写字符，准确率达91%。
Towards Scalable Handwriting Communication via EEG Decoding and Latent Embedding Integration
- 融合脑电与手部运动信息，提取稳定嵌入表征
- 九类手写符号识别准确率91%
- 适合脑机接口、神经解码研究者参考
近年来，脑机接口在解码多种运动相关任务方面取得进展，包括手势识别和运动分类，利用脑电图（EEG）数据。这些进展为理解神经信号如何被解析以识别特定身体动作奠定了基础。本研究聚焦于书写字母的分类任务，旨在解码与手写相关的脑电信号。为此，我们引入手部运动学信息，通过辅助变量（CEBRA）指导从高维神经记录中提取一致嵌入。这些CEBRA嵌入与原始脑电数据一同输入并行卷积神经网络模型，实现双源特征同步提取。模型对九种不同手写字符进行分类，涵盖感叹号、逗号等符号。采用五折交叉验证进行定量评估，并通过可视化分析嵌入空间结构。结果表明，该方法在九类任务中达到91%的分类准确率，验证了从脑电数据中解码精细手写内容的可行性。
原文摘要 · Abstract (English)
In recent years, brain-computer interfaces have made advances in decoding various motor-related tasks, including gesture recognition and movement classification, utilizing electroencephalogram (EEG) data. These developments are fundamental in exploring how neural signals can be interpreted to recognize specific physical actions. This study centers on a written alphabet classification task, where we aim to decode EEG signals associated with handwriting. To achieve this, we incorporate hand kinematics to guide the extraction of the consistent embeddings from high-dimensional neural recordings using auxiliary variables (CEBRA). These CEBRA embeddings, along with the EEG, are processed by a parallel convolutional neural network model that extracts features from both data sources simultaneously. The model classifies nine different handwritten characters, including symbols such as exclamation marks and commas, within the alphabet. We evaluate the model using a quantitative five-fold cross-validation approach and explore the structure of the embedding space through visualizations. Our approach achieves a classification accuracy of 91 % for the nine-class task, demonstrating the feasibility of fine-grained handwriting decoding from EEG.
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