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Brain-Computer Interface (BCI) is a system empowering humans to communicatewith or control the outside world with exclusively brain intentions.Electroencephalography (EEG) based BCIs are promising solutions due to theirconvenient and portable instruments. Motor imagery EEG (MI-EEG) is a kind ofmost widely focused EEG signals, which reveals a subjects movement intentionswithout actual actions. Despite the extensive research of MI-EEG in recentyears, it is still challenging to interpret EEG signals effectively due to themassive noises in EEG signals (e.g., low signal noise ratio and incomplete EEGsignals), and difficulties in capturing the inconspicuous relationships betweenEEG signals and certain brain activities. Most existing works either onlyconsider EEG as chain-like sequences neglecting complex dependencies betweenadjacent signals or performing simple temporal averaging over EEG sequences. Inthis paper, we introduce both cascade and parallel convolutional recurrentneural network models for precisely identifying human intended movements byeffectively learning compositional spatio-temporal representations of raw EEGstreams. The proposed models grasp the spatial correlations between physicallyneighboring EEG signals by converting the chain like EEG sequences into a 2Dmesh like hierarchy. An LSTM based recurrent network is able to extract thesubtle temporal dependencies of EEG data streams. Extensive experiments on alarge-scale MI-EEG dataset (108 subjects, 3,145,160 EEG records) havedemonstrated that both models achieve high accuracy near 98.3% and outperform aset of baseline methods and most recent deep learning based EEG recognitionmodels, yielding a significant accuracy increase of 18% in the cross-subjectvalidation scenario.

Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface