  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.
