Towards practical applications of Electroencephalography (EEG), lightweight acquisition devices garner significant attention. However, EEG channel selection methods are commonly data-sensitive and cannot establish a unified sound paradigm for EEG acquisition devices. Through reverse conceptualisation, we formulated EEG applications in an EEG super-resolution (SR) manner, but suffered from high computation costs, extra interpolation bias, and few insights into spatiotemporal dependency modelling. To this end, we propose ESTformer, an EEG SR framework that utilises spatiotemporal dependencies based on the transformer. ESTformer applies positional encoding methods and a multihead self-attention mechanism to the space and time dimensions, which can learn spatial structural correlations and temporal functional variations. ESTformer, with the fixed mask strategy, adopts a mask token to upsample low-resolution (LR) EEG data in the case of disturbance from mathematical interpolation methods. On this basis, we designed various transformer blocks to construct a spatial interpolation module (SIM) and a temporal reconstruction module (TRM). Finally, ESTformer cascades the SIM and TRM to capture and model the spatiotemporal dependencies for EEG SR with fidelity. Extensive experimental results on two EEG datasets show the effectiveness of ESTformer against previous state-of-the-art methods, demonstrating the versatility of the Transformer for EEG SR tasks. The superiority of the SR data was verified in an EEG-based person identification and emotion recognition task, achieving a 2% to 38% improvement compared with the LR data at different sampling scales.

翻译：面向脑电图（EEG）的实际应用，轻量级采集设备受到广泛关注。然而，EEG通道选择方法通常对数据敏感，且无法为EEG采集设备建立统一可靠的范式。通过逆向概念化，我们将EEG应用以EEG超分辨率（SR）的形式进行表述，但面临高计算成本、额外插值偏差以及对时空依赖性建模的深入理解不足等问题。为此，我们提出了ESTformer，一种基于Transformer的EEG SR框架，该框架利用时空依赖性。ESTformer在空间和时间维度上应用位置编码方法和多头自注意力机制，能够学习空间结构相关性和时间功能变化。ESTformer采用固定掩码策略，在数学插值方法存在干扰的情况下，使用掩码令牌对低分辨率（LR）EEG数据进行上采样。在此基础上，我们设计了多种Transformer块来构建空间插值模块（SIM）和时间重建模块（TRM）。最后，ESTformer级联SIM和TRM，以高保真度捕获和建模EEG SR的时空依赖性。在两个EEG数据集上的大量实验结果表明，ESTformer相较于先前最先进方法的有效性，展示了Transformer在EEG SR任务中的通用性。SR数据的优越性在基于EEG的个人身份识别和情绪识别任务中得到验证，在不同采样尺度下相比LR数据实现了2%至38%的性能提升。