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.

翻译：在脑电解码领域，提升人工神经网络（ANN）性能具有重大潜力。本研究提出了一种名为“权重冻结”的新型方法，该方法基于ANN正则化原理与神经科学先验知识。权重冻结的核心思想是通过在反向传播过程中冻结全连接层的特定权重，降低特定神经元对特定脑电任务决策过程的影响。具体而言，通过引入掩码矩阵与阈值，可在反向传播期间确定需冻结权重的比例。此外，通过将掩码权重置零，权重冻结不仅能够实现以全连接层作为分类器的网络中的稀疏连接，还可作为全连接层的有效正则化方法。我们通过采用三种不同ANN架构与三个广泛认可的脑电数据集进行实验，验证了权重冻结的有效性。该方法在所有测试数据集上的分类准确率均显著超越此前的最优性能。补充对照实验揭示了权重冻结实施前后性能差异的机理，并剖析了阈值在权重冻结过程中的影响。本研究证实，与传统全连接网络相比，权重冻结在脑电特征分类任务中具有更优效能。凭借其经证实的有效性，这一创新方法有望为未来脑电解码研究的进展做出重要贡献。