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Abstract：
The widely adopted paradigm in brain-computer interfaces (BCIs) involves motor imagery (MI), enabling improved communication between humans and machines. EEG signals derived from MI present several challenges due to their inherent characteristics, which lead to a complex process of classifying and finding the potential tasks of a specific participant. Another issue is that BCI systems can result in noisy data and redundant channels, which in turn can lead to increased equipment and computational costs. To address these problems, the optimal channel selection of a multiclass MI classification based on a Fusion convolutional neural network with Attention blocks (FCNNA) is proposed. In this study, we developed a CNN model consisting of layers of convolutional blocks with multiple spatial and temporal filters. These filters are designed specifically to capture the distribution and relationships of signal features across different electrode locations, as well as to analyze the evolution of these features over time. Following these layers, a Convolutional Block Attention Module (CBAM) is used to, further, enhance EEG signal feature extraction. In the process of channel selection, the genetic algorithm is used to select the optimal set of channels using a new technique to deliver fixed as well as variable channels for all participants. The proposed methodology is validated showing 6.41% improvement in multiclass classification compared to most baseline models. Notably, we achieved the highest results of 93.09% for binary classes involving left-hand and right-hand movements. In addition, the cross-subject strategy for multiclass classification yielded an impressive accuracy of 68.87%. Following channel selection, multiclass classification accuracy was enhanced, reaching 84.53%. Overall, our experiments illustrated the efficiency of the proposed EEG MI model in both channel selection and classification, showing superior results with either a full channel set or a reduced number of channels.
摘要：
脑机接口（BCI）中广泛采用的范式涉及运动图像（MI），改善人与机器之间的交流。脑电信号从MI产生，由于其固有的特点，提出了几个挑战，这导致了对特定参与者的潜在任务进行分类和查找的复杂过程。另一个问题是BCI系统可能导致嘈杂的数据和冗余通道，这反过来会导致设备和计算成本的增加。为了解决这些问题,提出了基于带有注意力块的融合卷积神经网络（FCNNA）的多类MI分类的最佳通道选择。在这项研究中,我们开发了一个由多层卷积块和多个时空滤波器组成的CNN模型。这些滤波器专门设计用于捕获不同电极位置的信号特征的分布和关系，以及分析这些特征随时间的演变。在这些层之后，卷积块注意模块(CBAM)用于，进一步,增强脑电信号特征提取。在频道选择的过程中,遗传算法用于选择最佳的渠道集，使用一种新技术为所有参与者提供固定和可变的渠道。所提出的方法经过验证，与大多数基线模型相比，多类分类提高了6.41％。值得注意的是,对于涉及左手和右手运动的二进制类，我们获得了93.09％的最高结果。此外,多类别分类的跨主题策略产生了68.87%的惊人准确率.选择频道后，多类分类精度得到提高，达到84.53%。总的来说,我们的实验说明了所提出的EEGMI模型在通道选择和分类方面的效率，显示出较好的结果，无论是一个完整的通道集或减少数量的通道。
