A subspace approach to learning recurrent features from brain activity

B. Vikrham Gowreesunker, Ahmed H. Tewfik, Vijay A. Tadipatri, James Ashe, Giuseppe Pellize, Rahul Gupta

    Research output: Contribution to journalArticlepeer-review

    6 Scopus citations


    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 brainmachine 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.

    Original languageEnglish (US)
    Article number5692835
    Pages (from-to)240-248
    Number of pages9
    JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
    Issue number3
    StatePublished - Jun 1 2011


    • Brain activity signals
    • brainmachine interface (BMI)
    • iterative subspace identification
    • sparse representation
    • time variability


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