Abstract—Brain Computer Interfaces (BCI) seek to measure brain signals in order to control computational or robotic devices, with important applications to motor disability. Electrocorticography (ECoG) is an emerging signal platform for long term implantation of a brain signal recording device, but current approaches rely heavily on screening tasks and trained technicians to find and specify repeatable features in the ECoG signal. Here we explore unsupervised approaches to reducing the ECoG signal stream into a few components that correspond most directly to neural patterns that correlate to subject task performance (neural correlates). We report on the development of a real-time feedback system we call the “Brain Mirror” which is based on the real time, incremental learning of a Deep Belief Network. On real patient data, we demonstrate that the components learned online with Deep Belief Networks have higher correlations with neural patterns than PCA.
Index Terms—Brain Computer Interface, Deep Belief Networks, Electrocortiogram, neural correlates, Unsupervised Learning.
Zachary V. Freudenburg is with the Department of Computer Science & Engineering Washington University in St. Louis, St. Louis, Missouri, USA, and also with the UMC Utrecht, Utrecht, The Netherlands, voges78@gmail.com. Nicolas F. Ramsey is with the Rudolf Magnus Institute, UMC Utrecht, Utrecht, The Netherlands, n.ramsey@umcutrecht.nl. Mark Wronkiewicz is at the Department of Biomedical Engineering at Washing University in St. Louis, St. Louis, Missouri, USA, mdw4@cec.wustl.edu. William D. Smart and Robert Pless are with the Department of Computer Science & Engineering at Washington University in St. Louis, St. Louis, Missouri, USA. wds@cse.wustl.edu, pless@cse.wustl.edu. Eric C. Leuthardt is with the departments of Neurosurgery and Biomedical Engineering at Washington University in St. Louis, St. Louis, Missouri, USA, LeuthardtE@nsurg.wustl.edu.
Cite: Zachary V. Freudenburg, Nicolas F. Ramsey, Mark Wronkiewicz, William D. Smart, Robert Pless,and Eric C. Leuthardt, "Real-time Naive Learning of Neural Correlates in ECoG Electrophysiology," International Journal of Machine Learning and Computing vol. 1, no. 3, pp. 269-278, 2011.