  In the realm of EEG decoding, enhancing the performance of artificial neural
networks (ANNs) carries significant potential. This study introduces a novel
approach, termed "weight freezing", that is anchored on the principles of ANN
regularization and neuroscience prior knowledge. The concept of weight freezing
revolves around the idea of reducing certain neurons' influence on the
decision-making process for a specific EEG task by freezing specific weights in
the fully connected layer during the backpropagation process. This is
actualized through the use of a mask matrix and a threshold to determine the
proportion of weights to be frozen during backpropagation. Moreover, by setting
the masked weights to zero, weight freezing can not only realize sparse
connections in networks with a fully connected layer as the classifier but also
function as an efficacious regularization method for fully connected layers.
Through experiments involving three distinct ANN architectures and three widely
recognized EEG datasets, we validate the potency of weight freezing. Our method
significantly surpasses previous peak performances in classification accuracy
across all examined datasets. Supplementary control experiments offer insights
into performance differences pre and post weight freezing implementation and
scrutinize the influence of the threshold in the weight freezing process. Our
study underscores the superior efficacy of weight freezing compared to
traditional fully connected networks for EEG feature classification tasks. With
its proven effectiveness, this innovative approach holds substantial promise
for contributing to future strides in EEG decoding research.

Machine Learning;Signal Processing