Literature DB >> 16792292

Electrocorticography-based brain computer interface--the Seattle experience.

Eric C Leuthardt1, Kai J Miller, Gerwin Schalk, Rajesh P N Rao, Jeffrey G Ojemann.   

Abstract

Electrocorticography (ECoG) has been demonstrated to be an effective modality as a platform for brain-computer interfaces (BCIs). Through our experience with ten subjects, we further demonstrate evidence to support the power and flexibility of this signal for BCI usage. In a subset of four patients, closed-loop BCI experiments were attempted with the patient receiving online feedback that consisted of one-dimensional cursor movement controlled by ECoG features that had shown correlation with various real and imagined motor and speech tasks. All four achieved control, with final target accuracies between 73%-100%. We assess the methods for achieving control and the manner in which enhancing online control can be accomplished by rescreening during online tasks. Additionally, we assess the relevant issues of the current experimental paradigm in light of their clinical constraints.

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Year:  2006        PMID: 16792292     DOI: 10.1109/TNSRE.2006.875536

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  60 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.  Cortical activity during motor execution, motor imagery, and imagery-based online feedback.

Authors:  Kai J Miller; Gerwin Schalk; Eberhard E Fetz; Marcel den Nijs; Jeffrey G Ojemann; Rajesh P N Rao
Journal:  Proc Natl Acad Sci U S A       Date:  2010-02-16       Impact factor: 11.205

Review 4.  The development of brain-machine interface neuroprosthetic devices.

Authors:  Parag G Patil; Dennis A Turner
Journal:  Neurotherapeutics       Date:  2008-01       Impact factor: 7.620

5.  Decoding Native Cortical Representations for Flexion and Extension at Upper Limb Joints Using Electrocorticography.

Authors:  Tessy M Thomas; Daniel N Candrea; Matthew S Fifer; David P McMullen; William S Anderson; Nitish V Thakor; Nathan E Crone
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2019-01-07       Impact factor: 3.802

6.  Comparison of subdural and subgaleal recordings of cortical high-gamma activity in humans.

Authors:  Jared D Olson; Jeremiah D Wander; Lise Johnson; Devapratim Sarma; Kurt Weaver; Edward J Novotny; Jeffrey G Ojemann; Felix Darvas
Journal:  Clin Neurophysiol       Date:  2015-04-09       Impact factor: 3.708

7.  Investigating the function of deep cortical and subcortical structures using stereotactic electroencephalography: lessons from the anterior cingulate cortex.

Authors:  Robert A McGovern; Tarini Ratneswaren; Elliot H Smith; Jennifer F Russo; Amy C Jongeling; Lisa M Bateman; Catherine A Schevon; Neil A Feldstein; Guy M McKhann; Sameer Sheth
Journal:  J Vis Exp       Date:  2015-04-15       Impact factor: 1.355

8.  Characterization of the effects of the human dura on macro- and micro-electrocorticographic recordings.

Authors:  David T Bundy; Erik Zellmer; Charles M Gaona; Mohit Sharma; Nicholas Szrama; Carl Hacker; Zachary V Freudenburg; Amy Daitch; Daniel W Moran; Eric C Leuthardt
Journal:  J Neural Eng       Date:  2014-02       Impact factor: 5.379

9.  Brain-machine interfaces and transcranial stimulation: future implications for directing functional movement and improving function after spinal injury in humans.

Authors:  Jose M Carmena; Leonardo G Cohen
Journal:  Handb Clin Neurol       Date:  2012

10.  Decoding continuous limb movements from high-density epidural electrode arrays using custom spatial filters.

Authors:  A R Marathe; D M Taylor
Journal:  J Neural Eng       Date:  2013-04-23       Impact factor: 5.379

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