LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface

Abstract

In this paper, we propose LGG, a neurologically inspired graph neural network, to learn local-global-graph representations from Electroencephalography (EEG) for a Brain-Computer Interface (BCI). A temporal convolutional layer with multi-scale 1D convolutional kernels and kernel-level attention fusion is proposed to learn the temporal dynamics of EEG. Inspired by neurological knowledge of cognitive processes in the brain, we propose local and global graph-filtering layers to learn the brain activities within and between different functional areas of the brain to model the complex relations among them during the cognitive processes. Under the robust nested cross-validation settings, the proposed method is evaluated on the publicly available dataset DEAP, and the classification performance is compared with state-of-the-art methods, such as FBFgMDM, FBTSC, Unsupervised learning, DeepConvNet, ShallowConvNet, EEGNet, and TSception. The results show that the proposed method outperforms all these state-of-the-art methods, and the improvements are statistically significant (p < 0.05) in most cases. The source code can be found at: https://github.com/yi-ding-cs/LGG

Graph Neural Network-Based EEG Classification: A Survey

“…[48], [52], [56], [60], [62]- [64], [66], [67], [69], [72], [76], [77], [79], [80], [83], [84], [86], [91], [94], [100], [104] An alternative categorisation of the brain graph structures is the functional (FC) and the "structural" connectivity (SC). Generally, SC graphs are pre-defined, whereas FC graphs can be both pre-defined and learnable.…”

“…Thus, it might not accurately represent the underlying brain network. Some papers propose to overcome this limitation by manually inserting global [53], [56]- [58], [62] or inter-hemispheric edges [46], [54], [87].…”

PGCN: Pyramidal Graph Convolutional Network for EEG Emotion Recognition

“…RGNN [13] used two regularizers, node-based domain adversarial training, and emotion-aware distribution learning, to optimize the crosssubject emotion recognition effect. Inspired by the neurological knowledge of brain cognitive processes, LGG-net [33] proposed local and global graphical filtering layers to learn brain activities within and between different brain functional regions to simulate the complex relationships in human brain cognitive processes, thus achieving the best emotion recognition effect. LR-GCN [34] employed self-attention forward updating Laplacian matrices and gradient backpropagation updating adjacency matrices to construct learnable brain electrode relationships jointly.…”

“…We disassembled and combined the local, mesoscopic and global modules in the PGCN to demonstrate the effect of each module on emotion recognition and show the results in Table V. In the ablation experiment, we only modified the feature extraction network composed of the local, mesoscopic, and global modules without changing other parts. In Table V, the backbone represents the most basic and commonly used twolayer GCN network based on 2D electrode adjacency matrix [33], and backbone module and local module do not activate at the same time.…”

“…Figure 6 shows the average prediction accuracy of the networks with different layer numbers. For vanilla GCN networks, as the layer number increases, the recognition accuracy tends to decrease after reaching a maximum of 92.34% at two layers, which is why the majority of current GCNs for emotion recognition tasks have two-layer networks [33], [14]. In the PGCN, as the number of network layers increases, the recognition accuracy reaches a maximum of 96.93% at the number of layers of five and begins to decline.…”