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，提升人工神经网络性能具有重要潜力。本研究提出了一种名为"权重冻结"的新方法，该方法基于人工神经网络正则化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中的特定权重，降低某些神经元对特定脑电任务决策过程的影响。这一目标通过使用掩码矩阵和阈值来确定反向传播过程中需冻结的权重比例得以实现。此外，通过将掩码权重设为零，权重冻结不仅能实现以全连接层作为分类器的网络中的稀疏连接，还能作为一种有效的全连接层正则化方法。通过涉及三种不同人工神经网络架构和三个广泛使用的脑电数据集的实验，我们验证了权重冻结的有效性。我们的方法在所有测试数据集上的分类准确率均显著超越了先前的最佳性能。辅助对比实验揭示了权重冻结实施前后性能差异的成因，并深入分析了阈值在权重冻结过程中的影响。本研究表明，在脑电特征分类任务中，权重冻结相较于传统全连接网络具有更优的效能。凭借其经过验证的有效性，这一创新方法将为脑电解码研究的未来发展做出重要贡献。