Weight Freezing: A Regularization Approach for Fully Connected Layers with an Application in EEG Classification

TL;DR

This paper introduces 'weight freezing', a regularization technique for fully connected layers in neural networks, which improves EEG classification accuracy by selectively freezing weights during training.

Contribution

The study proposes a novel weight freezing method that enhances EEG decoding performance by regularizing fully connected layers using a mask and threshold, validated across multiple architectures and datasets.

Findings

Weight freezing outperforms traditional fully connected networks in EEG classification.

The method achieves higher accuracy across three EEG datasets.

Control experiments reveal the impact of the freezing threshold on performance.

Abstract

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.