  Brain-computer interfaces (BCIs) harness electroencephalographic signals for
direct neural control of devices, offering a significant benefit for
individuals with motor impairments. Traditional machine learning methods for
EEG-based motor imagery (MI) classification encounter challenges such as manual
feature extraction and susceptibility to noise.This paper introduces
EEGEncoder, a deep learning framework that employs modified transformers and
TCNs to surmount these limitations. We innovatively propose a fusion
architecture, namely Dual-Stream Temporal-Spatial Block (DSTS), to capture
temporal and spatial features, improving the accuracy of Motor Imagery
classification task. Additionally, we use multiple parallel structures to
enhance the performance of the model. When tested on the BCI Competition IV-2a
dataset, our model results outperform current state-of-the-art techniques.
