Department of Computer Science, College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia.
Sensors (Basel). 2024 May 16;24(10):3168. doi: 10.3390/s24103168.
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.
脑机接口 (BCI) 中广泛采用的范式涉及运动想象 (MI)，能够促进人机之间的更好沟通。由于其固有特性，从 MI 中得出的 EEG 信号在分类和寻找特定参与者的潜在任务方面带来了一系列挑战。另一个问题是，BCI 系统可能会导致数据嘈杂和冗余通道，这反过来又会增加设备和计算成本。为了解决这些问题，提出了一种基于融合卷积神经网络注意力块（FCNNA）的多类 MI 分类的最优通道选择。在这项研究中，我们开发了一个由具有多个时空滤波器的卷积块层组成的 CNN 模型。这些滤波器专门设计用于捕捉不同电极位置的信号特征分布和关系，以及分析这些特征随时间的演变。在这些层之后，使用卷积块注意力模块 (CBAM) 进一步增强 EEG 信号特征提取。在通道选择过程中，遗传算法用于使用一种新技术选择最优的通道集，为所有参与者提供固定和可变通道。所提出的方法通过与大多数基准模型相比，在多类分类中提高了 6.41%，得到了验证。值得注意的是，我们在涉及左手和右手运动的二进制类中实现了 93.09%的最高结果。此外，对于多类分类的跨主体策略，达到了令人印象深刻的 68.87%的准确率。在进行通道选择后，多类分类的准确率得到了提高，达到了 84.53%。总体而言，我们的实验说明了所提出的 EEG MI 模型在通道选择和分类方面的效率，无论是使用完整的通道集还是减少的通道数，都能得到优越的结果。
