  Towards practical applications of Electroencephalography (EEG) data,
lightweight acquisition devices, equipped with a few electrodes, result in a
predicament where analysis methods can only leverage EEG data with extremely
low spatial resolution. Recent methods mainly focus on using mathematical
interpolation methods and Convolutional Neural Networks for EEG
super-resolution (SR), but they suffer from high computation costs, extra bias,
and few insights in spatiotemporal dependency modeling. To this end, we propose
the ESTformer, an EEG SR framework utilizing spatiotemporal dependencies based
on the Transformer. The ESTformer applies positional encoding methods and the
Multi-head Self-attention mechanism to the space and time dimensions, which can
learn spatial structural information and temporal functional variation. The
ESTformer, with the fixed masking strategy, adopts a mask token to up-sample
the low-resolution (LR) EEG data in case of disturbance from mathematical
interpolation methods. On this basis, we design various Transformer blocks to
construct the Spatial Interpolation Module (SIM) and the Temporal
Reconstruction Module (TRM). Finally, the ESTformer cascades the SIM and the
TRM to capture and model spatiotemporal dependencies for EEG SR with fidelity.
Extensive experimental results on two EEG datasets show the effectiveness of
the ESTformer against previous state-of-the-art methods and verify the
superiority of the SR data to the LR data in EEG-based downstream tasks of
person identification and emotion recognition. The proposed ESTformer
demonstrates the versatility of the Transformer for EEG SR tasks.
