Method and system for EEG motor imagery classification

A system comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive one or more Electroencephalogram (EEG) signals and corresponding ground truth labels comprising a ground truth graph label, a ground truth edge label, and a ground truth node label, wherein each of the one or more EEG signals comprise a plurality of channels;
• extract a plurality of temporal embeddings corresponding to each of the plurality of channels of each of the one or more EEG signals using a temporal feature extractor;
• construct one or more graphs corresponding to each of the one or more EEG signals, wherein each of the one or more graphs comprise a plurality of nodes corresponding to the plurality of channels and a weighted adjacency matrix defining connectivity between each pair of nodes among the plurality of nodes, and wherein each of the plurality of nodes is associated with the plurality of temporal embeddings of the corresponding channel;
• and
• iteratively train a Graph Neural Network (GNN), the weighted adjacency matrix, a graph classifier, a node classifier, and an edge classifier for a plurality of pre-defined number of iterations for classifying the one or more EEG signals by:
• generating a plurality of node embeddings corresponding to each of the one or more EEG signals by the GNN using the one or more graphs;
• classifying each of the one or more EEG signals based on the corresponding plurality of node embeddings using the graph classifier, the node classifier, and the edge classifier to obtain a graph label, a node label, and an edge label, wherein the graph label provides motor imagery classification, the node label provides quality of the EEG signal, and the edge label determines affinity between a pair of nodes among the plurality of nodes;
• and
• updating the GNN, the weighted adjacency matrix, the graph classifier, the node classifier, and the edge classifier based on a total loss obtained as a sum of a graph classification loss, a node classification loss, and an edge classification loss, wherein one or more hardware processors are configured to update the GNN, the weighted adjacency matrix, the graph classifier, the node classifier, and the edge classifier by:
• calculating a first, a second, a third, a fourth and a fifth gradient of the total loss with respect to parameters of the GNN, the weighted adjacency matrix, the graph classifier, the node classifier, and the edge classifier;
• and updating the GNN, the weighted adjacency matrix, the graph classifier, the node classifier, and the edge classifier using an optimizer based on the computed first, second, third, fourth and fifth gradient.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving one or more Electroencephalogram (EEG) signals and corresponding ground truth labels comprising a ground truth graph label, a ground truth edge label, and a ground truth node label, wherein each of the one or more EEG signals comprise a plurality of channels;
• extracting a plurality of temporal embeddings corresponding to each of the plurality of channels of each of the one or more EEG signals using a temporal feature extractor;
• constructing one or more graphs corresponding to each of the one or more EEG signals, wherein each of the one or more graphs comprise a plurality of nodes corresponding to the plurality of channels and a weighted adjacency matrix defining connectivity between each pair of nodes among the plurality of nodes, and wherein each of the plurality of nodes is associated with the plurality of temporal embeddings of the corresponding channel;
• and
• iteratively training a Graph Neural Network (GNN), the weighted adjacency matrix, a graph classifier, a node classifier, and an edge classifier for a plurality of pre-defined number of iterations for classifying the one or more EEG signals by:
• generating a plurality of node embeddings corresponding to each of the one or more EEG signals by the GNN using the one or more graphs;
• classifying each of the one or more EEG signals based on the corresponding plurality of node embeddings using the graph classifier, the node classifier, and the edge classifier to obtain a graph label, a node label, and an edge label, wherein the graph label provides motor imagery classification, the node label provides quality of the EEG signal, and the edge label determines affinity between a pair of nodes among the plurality of nodes;
• and
• updating the GNN, the weighted adjacency matrix, the graph classifier, the node classifier, and the edge classifier based on a total loss obtained as a sum of a graph classification loss, a node classification loss, and an edge classification loss, wherein the GNN, the weighted adjacency matrix, the graph classifier, the node classifier, and the edge classifier are updated by:
• calculating a first, a second, a third, a fourth and a fifth gradient of the total loss with respect to parameters of the GNN, the weighted adjacency matrix, the graph classifier, the node classifier, and the edge classifier;
• and
• updating the GNN, the weighted adjacency matrix, the graph classifier, the node classifier, and the edge classifier using an optimizer based on the computed first, second, third, fourth and fifth gradient.