  Neuropsychological studies suggest that co-operative activities among
different brain functional areas drive high-level cognitive processes. To learn
the brain activities within and among different functional areas of the brain,
we propose LGGNet, a novel neurologically inspired graph neural network, to
learn local-global-graph representations of electroencephalography (EEG) for
Brain-Computer Interface (BCI). The input layer of LGGNet comprises a series of
temporal convolutions with multi-scale 1D convolutional kernels and
kernel-level attentive fusion. It captures temporal dynamics of EEG which then
serves as input to the proposed local and global graph-filtering layers. Using
a defined neurophysiologically meaningful set of local and global graphs,
LGGNet models the complex relations within and among functional areas of the
brain. Under the robust nested cross-validation settings, the proposed method
is evaluated on three publicly available datasets for four types of cognitive
classification tasks, namely, the attention, fatigue, emotion, and preference
classification tasks. LGGNet is compared with state-of-the-art methods, such as
DeepConvNet, EEGNet, R2G-STNN, TSception, RGNN, AMCNN-DGCN, HRNN and GraphNet.
The results show that LGGNet outperforms these methods, and the improvements
are statistically significant (p<0.05) in most cases. The results show that
bringing neuroscience prior knowledge into neural network design yields an
improvement of classification performance. The source code can be found at
https://github.com/yi-ding-cs/LGG
