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Abstract：
This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain-computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, using multiclass classification based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and minimum-norm estimate algorithms were implemented to select discriminant channels and enhance the EEG data. Hence, deep EEGNet and convolutional recurrent neural networks were separately implemented to classify the EEG data for image visualization into 40 labels. Using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures. The satisfactory results obtained with this method offer a new implementation opportunity for multitask embedded BCI applications utilizing a reduced number of both channels (<50%) and network parameters (<110 K).
摘要：
这项工作解决了使用深度学习架构将多类视觉EEG信号分类为40类的脑机接口应用的挑战。视觉多类分类方法为BCI应用程序提供了显着的优势，因为它允许监督多个BCI交互。考虑到每个类标签监督一个BCI任务。然而,由于脑电信号的非线性和非平稳性，使用基于EEG特征的多类别分类仍然是BCI系统的重大挑战。在目前的工作中,实现了基于互信息的判别通道选择和最小范数估计算法，以选择判别通道并增强EEG数据。因此，分别实现了深度EEGNet和卷积递归神经网络，将用于图像可视化的EEG数据分类为40个标签。使用k折交叉验证方法，通过实施上述网络体系结构，平均分类准确率分别为94.8%和89.8%。使用该方法获得的令人满意的结果为多任务嵌入式BCI应用程序提供了新的实现机会，该应用程序利用了减少数量的通道（<50％）和网络参数（<110K）。
