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An electroencephalography (EEG) based Brain Computer Interface (BCI) enablespeople to communicate with the outside world by interpreting the EEG signals oftheir brains to interact with devices such as wheelchairs and intelligentrobots. More specifically, motor imagery EEG (MI-EEG), which reflects asubjects active intent, is attracting increasing attention for a variety of BCIapplications. Accurate classification of MI-EEG signals while essential foreffective operation of BCI systems, is challenging due to the significant noiseinherent in the signals and the lack of informative correlation between thesignals and brain activities. In this paper, we propose a novel deep neuralnetwork based learning framework that affords perceptive insights into therelationship between the MI-EEG data and brain activities. We design a jointconvolutional recurrent neural network that simultaneously learns robusthigh-level feature presentations through low-dimensional dense embeddings fromraw MI-EEG signals. We also employ an Autoencoder layer to eliminate variousartifacts such as background activities. The proposed approach has beenevaluated extensively on a large- scale public MI-EEG dataset and a limited buteasy-to-deploy dataset collected in our lab. The results show that our approachoutperforms a series of baselines and the competitive state-of-the- artmethods, yielding a classification accuracy of 95.53%. The applicability of ourproposed approach is further demonstrated with a practical BCI system fortyping.

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals