• Jul 03, 2017 News!Good News! Since 2017, IJMLC has been indexed by Scopus!
  • Aug 15, 2017 News![CFP] 2017 the annual meeting of IJMLC Editorial Board, ACMLC 2017, will be held in Singapore, December 8-10, 2017.   [Click]
  • Sep 09, 2017 News!Vol.7, No.4 has been published with online version.   [Click]
Search
General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2011 Vol.1(3): 269-278 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.40

Real-time Naive Learning of Neural Correlates in ECoG Electrophysiology

Zachary V. Freudenburg, Nicolas F. Ramsey, Mark Wronkiewicz, William D. Smart, Robert Pless, and Eric C. Leuthardt
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.

[PDF]

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.

Copyright © 2008-2015. International Journal of Machine Learning and Computing. All rights reserved.
E-mail: ijmlc@ejournal.net