Fusing Pretrained ViTs with TCNet for Enhanced EEG Regression

arXiv:2404.15311v1 Announce Type: cross
Abstract: 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 …

abstract accuracy analysis arxiv brain computer cs.ai cs.lg development dynamics eeg eess.sp however interfaces regression robust speed type