Abstract
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.
Abstract (translated)
近年来，脑机接口在解码各种与运动相关的任务方面取得了进展，包括通过脑电图（EEG）数据进行手势识别和动作分类。这些发展对于探索如何解读神经信号以识别特定的物理动作具有基础性意义。本研究专注于手写字母分类任务，旨在解码与书写相关的大脑电信号。为此，我们整合了手部运动学知识来指导利用辅助变量（CEBRA）从高维神经记录中提取一致嵌入的过程。这些CEBRA嵌入和EEG数据由一个并行卷积神经网络模型处理，该模型同时从两个数据源中提取特征。模型对包括感叹号和逗点等符号在内的九种不同手写字符进行分类。我们使用定量的五折交叉验证方法评估了模型，并通过可视化探索了嵌入空间的结构。我们的方法在九类任务上达到了91%的分类准确率，这证明了从EEG信号中精细解码手写动作的可行性。
URL
https://arxiv.org/abs/2411.09170
