Literature DB >> 20460690

Electroencephalographic (EEG) control of three-dimensional movement.

Dennis J McFarland1, William A Sarnacki, Jonathan R Wolpaw.   

Abstract

Brain-computer interfaces (BCIs) can use brain signals from the scalp (EEG), the cortical surface (ECoG), or within the cortex to restore movement control to people who are paralyzed. Like muscle-based skills, BCIs' use requires activity-dependent adaptations in the brain that maintain stable relationships between the person's intent and the signals that convey it. This study shows that humans can learn over a series of training sessions to use EEG for three-dimensional control. The responsible EEG features are focused topographically on the scalp and spectrally in specific frequency bands. People acquire simultaneous control of three independent signals (one for each dimension) and reach targets in a virtual three-dimensional space. Such BCI control in humans has not been reported previously. The results suggest that with further development noninvasive EEG-based BCIs might control the complex movements of robotic arms or neuroprostheses.

Entities:  

Mesh:

Year:  2010        PMID: 20460690      PMCID: PMC2907523          DOI: 10.1088/1741-2560/7/3/036007

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  38 in total

1.  Real-time control of a robotic arm by neuronal ensembles.

Authors:  E E Fetz
Journal:  Nat Neurosci       Date:  1999-07       Impact factor: 24.884

Review 2.  Brain-computer interfaces for communication and control.

Authors:  Jonathan R Wolpaw; Niels Birbaumer; Dennis J McFarland; Gert Pfurtscheller; Theresa M Vaughan
Journal:  Clin Neurophysiol       Date:  2002-06       Impact factor: 3.708

Review 3.  Motor areas in the frontal lobe of the primate.

Authors:  Richard P Dum; Peter L Strick
Journal:  Physiol Behav       Date:  2002-12

4.  BCI2000: a general-purpose brain-computer interface (BCI) system.

Authors:  Gerwin Schalk; Dennis J McFarland; Thilo Hinterberger; Niels Birbaumer; Jonathan R Wolpaw
Journal:  IEEE Trans Biomed Eng       Date:  2004-06       Impact factor: 4.538

5.  An EEG-based brain-computer interface for cursor control.

Authors:  J R Wolpaw; D J McFarland; G W Neat; C A Forneris
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1991-03

6.  A linear feature space for simultaneous learning of spatio-spectral filters in BCI.

Authors:  J Farquhar
Journal:  Neural Netw       Date:  2009-07-02

7.  Multichannel EEG-based brain-computer communication.

Authors:  J R Wolpaw; D J McFarland
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1994-06

8.  EEG-based communication: analysis of concurrent EMG activity.

Authors:  T M Vaughan; L A Miner; D J McFarland; J R Wolpaw
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1998-12

9.  Spatial filter selection for EEG-based communication.

Authors:  D J McFarland; L M McCane; S V David; J R Wolpaw
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1997-09

10.  Learning to control a brain-machine interface for reaching and grasping by primates.

Authors:  Jose M Carmena; Mikhail A Lebedev; Roy E Crist; Joseph E O'Doherty; David M Santucci; Dragan F Dimitrov; Parag G Patil; Craig S Henriquez; Miguel A L Nicolelis
Journal:  PLoS Biol       Date:  2003-10-13       Impact factor: 8.029

View more
  91 in total

1.  Active training paradigm for motor imagery BCI.

Authors:  Junhua Li; Liqing Zhang
Journal:  Exp Brain Res       Date:  2012-04-05       Impact factor: 1.972

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.  EEG control of a virtual helicopter in 3-dimensional space using intelligent control strategies.

Authors:  Audrey S Royer; Alexander J Doud; Minn L Rose; Bin He
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2010-09-27       Impact factor: 3.802

4.  Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface.

Authors:  Karl LaFleur; Kaitlin Cassady; Alexander Doud; Kaleb Shades; Eitan Rogin; Bin He
Journal:  J Neural Eng       Date:  2013-06-04       Impact factor: 5.379

5.  The advantages of the surface Laplacian in brain-computer interface research.

Authors:  Dennis J McFarland
Journal:  Int J Psychophysiol       Date:  2014-08-01       Impact factor: 2.997

6.  Exploring Cognitive Flexibility With a Noninvasive BCI Using Simultaneous Steady-State Visual Evoked Potentials and Sensorimotor Rhythms.

Authors:  Bradley J Edelman; Jianjun Meng; Nicholas Gulachek; Christopher C Cline; Bin He
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2018-05       Impact factor: 3.802

7.  Sensorimotor Rhythm BCI with Simultaneous High Definition-Transcranial Direct Current Stimulation Alters Task Performance.

Authors:  Bryan S Baxter; Bradley J Edelman; Nicholas Nesbitt; Bin He
Journal:  Brain Stimul       Date:  2016-07-15       Impact factor: 8.955

8.  High-performance neuroprosthetic control by an individual with tetraplegia.

Authors:  Jennifer L Collinger; Brian Wodlinger; John E Downey; Wei Wang; Elizabeth C Tyler-Kabara; Douglas J Weber; Angus J C McMorland; Meel Velliste; Michael L Boninger; Andrew B Schwartz
Journal:  Lancet       Date:  2012-12-17       Impact factor: 79.321

9.  Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces.

Authors:  Jun Lu; Dennis J McFarland; Jonathan R Wolpaw
Journal:  J Neural Eng       Date:  2012-12-10       Impact factor: 5.379

10.  Three-Dimensional Brain-Computer Interface Control Through Simultaneous Overt Spatial Attentional and Motor Imagery Tasks.

Authors:  Jianjun Meng; Taylor Streitz; Nicholas Gulachek; Daniel Suma; Bin He
Journal:  IEEE Trans Biomed Eng       Date:  2018-10-01       Impact factor: 4.538

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.