Apr 06, 2018 | 14:00—15:30
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 large-scale 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-the art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.
Salil Kanhere received his M.S. and Ph.D. degrees, both in Electrical Engineering from Drexel University, Philadelphia. He is currently an Associate Professor in the School of Computer Science and Engineering at UNSW Sydney, Australia. His current research interests include Internet of Things, pervasive computing, blockchain, crowdsourcing, data analytics, privacy and security. He has published over 180 peer-reviewed articles and delivered over 20 tutorials and keynote talks on these research topics. His research has been featured on ABC News, IEEE Spectrum, Wired, ZDNET and other media outlets. Salil regularly serves on the organizing committee of a number of IEEE and ACM international conferences. He is on the Editorial Board of Elsevier’s Pervasive and Mobile Computing and Computer Communications. Salil is a Senior Member of both the IEEE and the ACM. He is a recipient of the Humboldt Research Fellowship in 2014.