Intention Recognition from Spatio-Temporal Representation of EEG Signals

The motor imagery brain-computer interface uses the human brain intention to achieve better control. The main technical problems are feature representation and classification of signal features for specific thinking activities. Inspired by the structure and function of the human brain, we construct a neural computing model to explore the critical issues in the representation and real-time recognition of the state of specific thinking activities. In consideration of the physiological structure and the information processing process of the brain, we construct a multi-scale cascaded Conv-GRU model and extract high-resolution feature information from the dual spatio-temporal dimension, effectively removing signal noise, improving the signal-to-noise ratio, and reducing information loss. Extensive experiments demonstrate that our model has a low dependence on training data size and outperforms state-of-the-art multi-intention recognition methods