Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System, Xi'an Jiaotong University, Xi'an 710049, China.
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2023 Mar 6;23(5):2863. doi: 10.3390/s23052863.
An error-related potential (ErrP) occurs when people's expectations are not consistent with the actual outcome. Accurately detecting ErrP when a human interacts with a BCI is the key to improving these BCI systems. In this paper, we propose a multi-channel method for error-related potential detection using a 2D convolutional neural network. Multiple channel classifiers are integrated to make final decisions. Specifically, every 1D EEG signal from the anterior cingulate cortex (ACC) is transformed into a 2D waveform image; then, a model named attention-based convolutional neural network (AT-CNN) is proposed to classify it. In addition, we propose a multi-channel ensemble approach to effectively integrate the decisions of each channel classifier. Our proposed ensemble approach can learn the nonlinear relationship between each channel and the label, which obtains 5.27% higher accuracy than the majority voting ensemble approach. We conduct a new experiment and validate our proposed method on a Monitoring Error-Related Potential dataset and our dataset. With the method proposed in this paper, the accuracy, sensitivity and specificity were 86.46%, 72.46% and 90.17%, respectively. The result shows that the AT-CNNs-2D proposed in this paper can effectively improve the accuracy of ErrP classification, and provides new ideas for the study of classification of ErrP brain-computer interfaces.
当人们的期望与实际结果不一致时，就会出现错误相关电位（ErrP）。在人机交互时准确检测 ErrP 是提高这些脑机接口系统性能的关键。本文提出了一种基于二维卷积神经网络的多通道 ErrP 检测方法。多个通道分类器集成以做出最终决策。具体来说，将来自前扣带皮层（ACC）的每一个 1D EEG 信号转换为二维波形图像；然后，提出了一种名为基于注意力的卷积神经网络（AT-CNN）的模型对其进行分类。此外，我们提出了一种多通道集成方法，以有效整合每个通道分类器的决策。我们的集成方法可以学习每个通道和标签之间的非线性关系，从而比多数投票集成方法提高了 5.27%的准确率。我们进行了一项新的实验，并在 Monitoring Error-Related Potential 数据集和我们的数据集上验证了我们提出的方法。使用本文提出的方法，准确率、灵敏度和特异性分别为 86.46%、72.46%和 90.17%。结果表明，本文提出的 AT-CNNs-2D 可以有效提高 ErrP 分类的准确性，为 ErrP 脑机接口分类的研究提供了新的思路。
