By Topic

A Subspace Approach to Learning Recurrent Features From Brain Activity

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Gowreesunker, B.V. ; Syst. & Applic. R&D Center, Texas Instrum. Inc., Dallas, TX, USA ; Tewfik, A.H. ; Tadipatri, V.A. ; Ashe, J.
more authors

This paper introduces a novel technique to address the instability and time variability challenges associated with brain activity recorded on different days. A critical challenge when working with brain signal activity is the variability in their characteristics when the signals are collected in different sessions separated by a day or more. Such variability is due to the acute and chronic responses of the brain tissue after implantation, variations as the subject learns to optimize performance, physiological changes in a subject due to prior activity or rest periods and environmental conditions. We propose a novel approach to tackle signal variability by focusing on learning subspaces which are recurrent over time. Furthermore, we illustrate how we can use projections on those subspaces to improve classification for an application such as brain-machine interface (BMI). In this paper, we illustrate the merits of finding recurrent subspaces in the context of movement direction decoding using local field potential (LFP). We introduce two methods for using the learned subspaces in movement direction decoding and show a decoding power improvement from 76% to 88% for a particularly unstable subject and consistent decoding across subjects.

Published in:

Neural Systems and Rehabilitation Engineering, IEEE Transactions on  (Volume:19 ,  Issue: 3 )