Recent attempts at creating Foundation Models (FMs) for Electroencephalography (EEG) have achieved state-of-the-art performance on multiple tasks including Motor Imagery (MI). These MI tasks have typically involved coarse classification between imagined limb movements. However, the development of foundation models necessitates diverse datasets, both for pretraining and evaluating the progress of these models. In this work, we propose handwriting decoding as a challenging motor task for FMs. We show that several existing datasets are potentially confounded, and introduce a dataset that more rigorously evaluates models. On this dataset, we find that current FMs, despite showing SOTA performance in multiple MI datasets are outperformed by smaller task-specific models. We also highlight challenges specific to EEG-based handwriting decoding to inform future work. In our 4-letter classification task, we show that (a) Knowledge of movement-onset is crucial to reported decoding performance in prior works, with average performance across subjects dropping from $41.3\%$ to $32.4\%$. (b) Increasing test-time signal quality provides significant performance improvements ($45\%$ to $78\%$ in our best subject) compared to scaling training data with single-trial EEG. (c) While scaling training data steadily improves decoding performance, existing FMs do not outperform specialist models in handwriting decoding. We make our code available at https://anonymous.4open.science/r/EEG-Handwriting-BCI-DFCD/

翻译：近期构建脑电图（EEG）基础模型（FMs）的尝试已在包括运动想象（MI）在内的多项任务上取得了最先进性能。这些MI任务通常涉及想象肢体运动的粗粒度分类。然而，基础模型的发展需要多样化的数据集，既用于预训练也用于评估模型进展。本研究提出将手写解码作为基础模型的一项挑战性运动任务。我们发现现有多个数据集可能存在混淆因素，并引入了一个更严格评估模型的数据集。在该数据集上，我们发现当前的基础模型尽管在多个MI数据集中表现优异，但其性能却被更小的任务专用模型所超越。我们还强调了基于EEG的手写解码所特有的挑战，以指导未来工作。在四字母分类任务中，我们证明：（a）运动起始时间的知识是先前工作中报告解码性能的关键，受试者平均性能从$41.3\%$降至$32.4\%$；（b）与通过单次试验EEG扩展训练数据相比，提高测试时的信号质量可带来显著的性能提升（最佳受试者从$45\%$增至$78\%$）；（c）尽管扩展训练数据能稳步提升解码性能，但现有基础模型在手写解码中仍未超越专用模型。我们已将代码开源：https://anonymous.4open.science/r/EEG-Handwriting-BCI-DFCD/