  In previous studies, decoding electroencephalography (EEG) signals has not
considered the topological relationship of EEG electrodes. However, the latest
neuroscience has suggested brain network connectivity. Thus, the exhibited
interaction between EEG channels might not be appropriately measured via
Euclidean distance. To fill the gap, an attention-based graph residual network,
a novel structure of Graph Convolutional Neural Network (GCN), was presented to
detect human motor intents from raw EEG signals, where the topological
structure of EEG electrodes was built as a graph. Meanwhile, deep residual
learning with a full-attention architecture was introduced to address the
degradation problem concerning deeper networks in raw EEG motor imagery (MI)
data. Individual variability, the critical and longstanding challenge
underlying EEG signals, has been successfully handled with the state-of-the-art
performance, 98.08% accuracy at the subject level, 94.28% for 20 subjects.
Numerical results were promising that the implementation of the
graph-structured topology was superior to decode raw EEG data. The innovative
deep learning approach was expected to entail a universal method towards both
neuroscience research and real-world EEG-based practical applications, e.g.,
seizure prediction.
