Graph signal processing (GSP) provides signal analytic tools for data defined in irregular domains, as is the case of non-invasive electroencephalography (EEG). In this work, the recently introduced technique of Graph Slepian functions is exploited for the robust decoding of motor imagery (MI) brain activity. The particular technique builds over the concept of graph Fourier transform (GFT) and provides additional flexibility in the subsequent data analysis by incorporating domain knowledge. Based on contrastive learning, we introduce an algorithmic pipeline that attains a data driven and subject specific design of Graph Slepian functions. These functions, by incorporating both the topology of the sensor array and the empirical evidence about the differential functional covariation, act as spatial filters that enhance the information conveyed by the multichannel signal and specifically relates to the participant’s intention. The proposed technique for crafting Graph Slepians is incorporated in a MI-decoding scheme, in which the informed projections are fed to a support vector machine (SVM) that casts a prediction regarding the type of intended movement. The employed MI-decoder is evaluated based on two publicly available datasets and its superiority against popular alternatives in the field is established. Computational efficiency is listed among its main advantages, since it involves only simple matrix operations, allowing to consider its use in real-time implementations.

中文翻译：

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电机图像解码的协方差通知图斯皮尔安

图形信号处理（GSP）为非规则脑电图（EEG）的情况提供了用于在不规则域中定义的数据的信号分析工具。在这项工作中，最近引入的技术图Slepian函数用于运动图像（MI）脑活动的鲁棒解码。该特定技术建立在图傅立叶变换（GFT）概念的基础上，并通过合并领域知识在后续数据分析中提供了额外的灵活性。在对比学习的基础上，我们介绍了一种算法流水线，该流水线实现了数据驱动和Graph Slepian函数的主题特定设计。这些功能通过结合传感器阵列的拓扑结构和有关差分功能协变的经验证据，可以充当空间滤波器，从而增强多通道信号所传达的信息，并特别涉及参与者的意图。MI解码方案中包含了拟议的图斯普林斯技巧的拟定技术，在这种情况下，将通知的投影馈送到支持向量机（SVM），后者对预期移动的类型进行预测。基于两个可公开获得的数据集对所采用的MI解码器进行评估，并确定了其相对于该领域流行替代方案的优越性。计算效率是其主要优点之一，因为它仅涉及简单的矩阵运算，因此可以考虑将其用于实时实现中。