Zhang Zhijun, He Yu, Mai Weijian, Luo Yamei, Li Xiaoli, Cheng Yuanxiong, Huang Xiaoming, Lin Run
IEEE Trans Neural Netw Learn Syst. 2025 May;36(5):8166-8177. doi: 10.1109/TNNLS.2024.3437676. Epub 2025 May 2.
The brain signal classification is the basis for the implementation of brain-computer interfaces (BCIs). However, most existing brain signal classification methods are based on signal processing technology, which require a significant amount of manual intervention, such as channel selection and dimensionality reduction, and often struggle to achieve satisfactory classification accuracy. To achieve high classification accuracy and as little manual intervention as possible, a convolutional dynamically convergent differential neural network (ConvDCDNN) is proposed for solving the electroencephalography (EEG) signal classification problem. First, a single-layer convolutional neural network is used to replace the preprocessing steps in previous work. Then, focal loss is used to overcome the imbalance in the dataset. After that, a novel automatic dynamic convergence learning (ADCL) algorithm is proposed and proved for training neural networks. Experimental results on the BCI Competition 2003, BCI Competition III A, and BCI Competition III B datasets demonstrate that the proposed ConvDCDNN framework achieved state-of-the-art performance with accuracies of 100%, 99%, and 98%, respectively. In addition, the proposed algorithm exhibits a higher information transfer rate (ITR) compared with current algorithms.
脑信号分类是实现脑机接口（BCI）的基础。然而，现有的大多数脑信号分类方法都基于信号处理技术，这需要大量的人工干预，如通道选择和降维，并且常常难以达到令人满意的分类准确率。为了实现高分类准确率并尽可能减少人工干预，提出了一种卷积动态收敛差分神经网络（ConvDCDNN）来解决脑电图（EEG）信号分类问题。首先，使用单层卷积神经网络取代先前工作中的预处理步骤。然后，使用焦点损失来克服数据集中的不平衡问题。之后，提出并证明了一种新颖的自动动态收敛学习（ADCL）算法用于训练神经网络。在BCI竞赛2003、BCI竞赛III A和BCI竞赛III B数据集上的实验结果表明，所提出的ConvDCDNN框架分别以100%、99%和98%的准确率达到了当前最优性能。此外，与当前算法相比，所提出的算法具有更高的信息传输率（ITR）。
