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

Abstract

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subject's active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a largescale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-theart methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.

Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning

“…Deep learning has been recently attracting much attention in both academia and industry, due to its excellent performance on various research areas such as computer vision, speech recognition, natural language processing, and braincomputer interface [15]. Nevertheless, deep learning faces an important challenge that the performance of the algorithm highly depends on the selection of hyper-parameters.…”

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