Literature DB >> 19569888

Robust, long-term control of an electrocorticographic brain-computer interface with fixed parameters.

Tim Blakely1, Kai J Miller, Stavros P Zanos, Rajesh P N Rao, Jeffrey G Ojemann.   

Abstract

All previous multiple-day brain-computer interface (BCI) experiments have dynamically adjusted the parameterization between the signals measured from the brain and the features used to control the interface. The authors present the results of a multiple-day electrocorticographic (ECoG) BCI experiment. A patient with a subdural electrode array implanted for seizure localization performed tongue motor tasks. After an initial screening and feature selection on the 1st day, 5 consecutive days of cursor-based feedback were performed with a fixed parameterization. Control of the interface was robust throughout all days, with performance increasing to a stable state in which high-frequency ECoG signal could immediately be translated into cursor control. These findings demonstrate that ECoG-based BCIs can be implemented for multiple-day control without the necessity for sophisticated retraining and adaptation.

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Mesh:

Year:  2009        PMID: 19569888     DOI: 10.3171/2009.4.FOCUS0977

Source DB:  PubMed          Journal:  Neurosurg Focus        ISSN: 1092-0684            Impact factor:   4.047


  38 in total

1.  Control of a visual keyboard using an electrocorticographic brain-computer interface.

Authors:  Dean J Krusienski; Jerry J Shih
Journal:  Neurorehabil Neural Repair       Date:  2010-10-04       Impact factor: 3.919

Review 2.  Brain-computer interfaces in medicine.

Authors:  Jerry J Shih; Dean J Krusienski; Jonathan R Wolpaw
Journal:  Mayo Clin Proc       Date:  2012-02-10       Impact factor: 7.616

3.  Spanning the rich spectrum of the human brain: slow waves to gamma and beyond.

Authors:  Sarang S Dalal; Juan R Vidal; Carlos M Hamamé; Tomás Ossandón; Olivier Bertrand; Jean-Philippe Lachaux; Karim Jerbi
Journal:  Brain Struct Funct       Date:  2011-03-25       Impact factor: 3.270

Review 4.  Physiological properties of brain-machine interface input signals.

Authors:  Marc W Slutzky; Robert D Flint
Journal:  J Neurophysiol       Date:  2017-06-14       Impact factor: 2.714

5.  Sleep spindles are locally modulated by training on a brain-computer interface.

Authors:  Lise A Johnson; Tim Blakely; Dora Hermes; Shahin Hakimian; Nick F Ramsey; Jeffrey G Ojemann
Journal:  Proc Natl Acad Sci U S A       Date:  2012-10-22       Impact factor: 11.205

6.  Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects.

Authors:  Joseph N Mak; Jonathan R Wolpaw
Journal:  IEEE Rev Biomed Eng       Date:  2009

7.  Electrocorticographic amplitude predicts finger positions during slow grasping motions of the hand.

Authors:  Soumyadipta Acharya; Matthew S Fifer; Heather L Benz; Nathan E Crone; Nitish V Thakor
Journal:  J Neural Eng       Date:  2010-05-20       Impact factor: 5.379

8.  Affective Brain-Computer Interfaces As Enabling Technology for Responsive Psychiatric Stimulation.

Authors:  Alik S Widge; Darin D Dougherty; Chet T Moritz
Journal:  Brain Comput Interfaces (Abingdon)       Date:  2014-04-01

9.  Short-time windowed covariance: a metric for identifying non-stationary, event-related covariant cortical sites.

Authors:  Timothy Blakely; Jeffrey G Ojemann; Rajesh P N Rao
Journal:  J Neurosci Methods       Date:  2013-11-06       Impact factor: 2.390

10.  Decoding hand movement velocity from electroencephalogram signals during a drawing task.

Authors:  Jun Lv; Yuanqing Li; Zhenghui Gu
Journal:  Biomed Eng Online       Date:  2010-10-28       Impact factor: 2.819

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