García-Murillo Daniel Guillermo, Álvarez-Meza Andrés Marino, Castellanos-Dominguez Cesar German
Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia.
Diagnostics (Basel). 2023 Mar 16;13(6):1122. doi: 10.3390/diagnostics13061122.
This paper uses EEG data to introduce an approach for classifying right and left-hand classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity Network (KCS-FCnet) method addresses these limitations by providing richer spatial-temporal-spectral feature maps, a simpler architecture, and a more interpretable approach for EEG-driven MI discrimination. In particular, KCS-FCnet uses a single 1D-convolutional-based neural network to extract temporal-frequency features from raw EEG data and a cross-spectral Gaussian kernel connectivity layer to model channel functional relationships. As a result, the functional connectivity feature map reduces the number of parameters, improving interpretability by extracting meaningful patterns related to MI tasks. These patterns can be adapted to the subject's unique characteristics. The validation results prove that introducing KCS-FCnet shallow architecture is a promising approach for EEG-based MI classification with the potential for real-world use in brain-computer interface systems.
本文使用脑电图（EEG）数据介绍了一种在运动想象（MI）任务中对右手和左手类别进行分类的方法。内核互谱功能连接网络（KCS-FCnet）方法通过提供更丰富的时空谱特征图、更简单的架构以及用于脑电图驱动的MI辨别更具可解释性的方法来解决这些局限性。具体而言，KCS-FCnet使用单个基于一维卷积的神经网络从原始EEG数据中提取时频特征，并使用互谱高斯核连接层对通道功能关系进行建模。结果，功能连接特征图减少了参数数量，通过提取与MI任务相关的有意义模式提高了可解释性。这些模式可以适应受试者的独特特征。验证结果证明，引入KCS-FCnet浅层架构是一种有前途的基于脑电图的MI分类方法，具有在脑机接口系统中实际应用的潜力。
