  Recognition accuracy and response time are both critically essential ahead of
building practical electroencephalography (EEG) based brain-computer interface
(BCI). Recent approaches, however, have either compromised in the
classification accuracy or responding time. This paper presents a novel deep
learning approach designed towards remarkably accurate and responsive motor
imagery (MI) recognition based on scalp EEG. Bidirectional Long Short-term
Memory (BiLSTM) with the Attention mechanism manages to derive relevant
features from raw EEG signals. The connected graph convolutional neural network
(GCN) promotes the decoding performance by cooperating with the topological
structure of features, which are estimated from the overall data. The
0.4-second detection framework has shown effective and efficient prediction
based on individual and group-wise training, with 98.81% and 94.64% accuracy,
respectively, which outperformed all the state-of-the-art studies. The
introduced deep feature mining approach can precisely recognize human motion
intents from raw EEG signals, which paves the road to translate the EEG based
MI recognition to practical BCI systems.
