EEG signal classification in BCI based on graph theory and Riemannian learning

Machine Learning (ML) classifiers have been made more robust in recent years by leveraging the graph structure between the inputs using Neural Structured Learning (NSL). However, researchers have not taken full advantage of it for the Brain Computer Interface (BCI) classification tasks. While the traditional NSL faces the issues of a much minimized use of graph structural properties and optimal similarity metric, in this paper, we propose a Node Impact Multi Metric Threshold NSL (NI-MT-NSL) to overcome these issues. For the first time, the node-influence properties from graph theory are incorporated to alter the way different EEG samples influence the training, while an ensemble of semantic graphs is used in the NSL module to capture different semantic relations between the EEG trial data. The proposed model is assessed on the standard BCI IV ۲a dataset. On comparing its test accuracies with the traditional Riemannian classifiers and the baseline NSL, we have found improved accuracies over all subjects. We have found a tremendous improvement in classification, with a mean gain of ۷% for the subjects having very poor accuracy even with state-of-the-art methods.