The task of Electroencephalogram (EEG) analysis is paramount to the development of Brain-Computer Interfaces (BCIs). However, to reach the goal of developing robust, useful BCIs depends heavily on the speed and the accuracy at which BCIs can understand neural dynamics. In response to that goal, this paper details the integration of pre-trained Vision Transformers (ViTs) with Temporal Convolutional Networks (TCNet) to enhance the precision of EEG regression. The core of this approach lies in harnessing the sequential data processing strengths of ViTs along with the superior feature extraction capabilities of TCNet, to significantly improve EEG analysis accuracy. In addition, we analyze the importance of how to construct optimal patches for the attention mechanism to analyze, balancing both speed and accuracy tradeoffs. Our results showcase a substantial improvement in regression accuracy, as evidenced by the reduction of Root Mean Square Error (RMSE) from 55.4 to 51.8 on EEGEyeNet's Absolute Position Task, outperforming existing state-of-the-art models. Without sacrificing performance, we increase the speed of this model by an order of magnitude (up to 4.32x faster). This breakthrough not only sets a new benchmark in EEG regression analysis but also opens new avenues for future research in the integration of transformer architectures with specialized feature extraction methods for diverse EEG datasets.

翻译：脑电图（EEG）分析对于脑机接口（BCI）的发展至关重要。然而，要实现开发稳健、实用BCI的目标，很大程度上取决于BCI理解神经动力学的速度与准确性。为此，本文详述了将预训练视觉Transformer（ViT）与时序卷积网络（TCNet）相结合的方法，以提升EEG回归的精度。该方法的核心在于利用ViT处理序列数据的优势，结合TCNet卓越的特征提取能力，从而显著提高EEG分析的准确性。此外，我们分析了如何为注意力机制构建最优数据片段以进行分析的重要性，权衡了速度与精度之间的平衡。我们的结果显示回归精度得到显著提升，体现在EEGEyeNet绝对位置任务上的均方根误差（RMSE）从55.4降至51.8，超越了现有最先进模型。在不牺牲性能的前提下，我们将该模型的速度提升了一个数量级（最高达4.32倍加速）。这一突破不仅为EEG回归分析设立了新的基准，也为未来研究Transformer架构与专用特征提取方法在不同EEG数据集中的融合开辟了新途径。