EEG-GAT: Graph Attention Networks for Classification of Electroencephalogram (EEG) Signals.
Annu Int Conf IEEE Eng Med Biol Soc
; 2022: 30-35, 2022 07.
Article
en En
| MEDLINE
| ID: mdl-36086201
ABSTRACT
Graph neural networks (GNN) are an emerging framework in the deep learning community. In most GNN applications, the graph topology of data samples is provided in the dataset. Specifically, the graph shift operator (GSO), which could be adjacency, graph Laplacian, or their normalizations, is known a priori. However we often have no knowledge of the grand-truth graph topology underlying real-world datasets. One example of this is to extract subject-invariant features from physiological electroencephalogram (EEG) to predict a cognitive task. Previous methods use electrode sites to represent a node in the graph and connect them in various ways to hand-engineer a GSO e.g., i) each pair of electrode sites is connected to form a complete graph, ii) a specific number of electrode sites are connected to form a k-nearest neighbor graph, iii) each pair of electrode site is connected only if the Euclidean distance is within a heuristic threshold. In this paper, we overcome this limitation by parameterizing the GSO using a multi-head attention mechanism to explore the functional neural connectivity subject to a cognitive task between different electrode sites, and simultaneously learn the unsupervised graph topology in conjunction with the parameters of graph convolutional kernels.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
/
Electroencefalografía
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2022
Tipo del documento:
Article