Literature DB >> 12821178

Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans.

Hesham Sheikh1, Dennis J McFarland, William A Sarnacki, Jonathan R Wolpaw.   

Abstract

People can learn to control electroencephalographic (EEG) sensorimotor rhythm amplitude so as to move a cursor to select among choices on a computer screen. We explored the dependence of system performance on EEG control. Users moved the cursor to reach a target at one of four possible locations. EEG control was measured as the correlation (r(2)) between rhythm amplitude and target location. Performance was measured as accuracy (% of targets hit) and as information transfer rate (bits/trial). The relationship between EEG control and accuracy can be approximated by a linear function that is constant for all users. The results facilitate offline predictions of the effects on performance of using different EEG features or combinations of features to control cursor movement.

Entities:  

Mesh:

Year:  2003        PMID: 12821178     DOI: 10.1016/s0304-3940(03)00470-1

Source DB:  PubMed          Journal:  Neurosci Lett        ISSN: 0304-3940            Impact factor:   3.046


  11 in total

1.  Self-selected conscious strategies do not modulate motor cortical output during action observation.

Authors:  Katherine R Naish; Sukhvinder S Obhi
Journal:  J Neurophysiol       Date:  2015-08-26       Impact factor: 2.714

2.  An enhanced time-frequency-spatial approach for motor imagery classification.

Authors:  Nobuyuki Yamawaki; Christopher Wilke; Zhongming Liu; Bin He
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2006-06       Impact factor: 3.802

3.  Towards an independent brain-computer interface using steady state visual evoked potentials.

Authors:  Brendan Z Allison; Dennis J McFarland; Gerwin Schalk; Shi Dong Zheng; Melody Moore Jackson; Jonathan R Wolpaw
Journal:  Clin Neurophysiol       Date:  2008-02       Impact factor: 3.708

4.  Comparison of feature selection and classification methods for a brain-computer interface driven by non-motor imagery.

Authors:  Alvaro Fuentes Cabrera; Dario Farina; Kim Dremstrup
Journal:  Med Biol Eng Comput       Date:  2009-12-30       Impact factor: 2.602

5.  Sensorimotor rhythm-based brain-computer interface (BCI): model order selection for autoregressive spectral analysis.

Authors:  Dennis J McFarland; Jonathan R Wolpaw
Journal:  J Neural Eng       Date:  2008-04-22       Impact factor: 5.379

6.  Emulation of computer mouse control with a noninvasive brain-computer interface.

Authors:  Dennis J McFarland; Dean J Krusienski; William A Sarnacki; Jonathan R Wolpaw
Journal:  J Neural Eng       Date:  2008-03-05       Impact factor: 5.379

7.  Use of phase-locking value in sensorimotor rhythm-based brain-computer interface: zero-phase coupling and effects of spatial filters.

Authors:  Wenjuan Jian; Minyou Chen; Dennis J McFarland
Journal:  Med Biol Eng Comput       Date:  2017-03-25       Impact factor: 2.602

8.  Real-time two-dimensional asynchronous control of a computer cursor with a single subdural electrode.

Authors:  César Márquez-Chin; Milos R Popovic; Egor Sanin; Robert Chen; Andres M Lozano
Journal:  J Spinal Cord Med       Date:  2012-09       Impact factor: 1.985

9.  A scanning protocol for a sensorimotor rhythm-based brain-computer interface.

Authors:  Elisabeth V C Friedrich; Dennis J McFarland; Christa Neuper; Theresa M Vaughan; Peter Brunner; Jonathan R Wolpaw
Journal:  Biol Psychol       Date:  2008-08-22       Impact factor: 3.251

10.  Evaluation of a modified Fitts law brain-computer interface target acquisition task in able and motor disabled individuals.

Authors:  E A Felton; R G Radwin; J A Wilson; J C Williams
Journal:  J Neural Eng       Date:  2009-08-21       Impact factor: 5.379

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