Literature DB >> 20168002

Relationship between speed and EEG activity during imagined and executed hand movements.

Han Yuan1, Christopher Perdoni, Bin He.   

Abstract

The relationship between primary motor cortex and movement kinematics has been shown in nonhuman primate studies of hand reaching or drawing tasks. Studies have demonstrated that the neural activities accompanying or immediately preceding the movement encode the direction, speed and other information. Here we investigated the relationship between the kinematics of imagined and actual hand movement, i.e. the clenching speed, and the EEG activity in ten human subjects. Study participants were asked to perform and imagine clenching of the left hand and right hand at various speeds. The EEG activity in the alpha (8-12 Hz) and beta (18-28 Hz) frequency bands were found to be linearly correlated with the speed of imagery clenching. Similar parametric modulation was also found during the execution of hand movements. A single equation relating the EEG activity to the speed and the hand (left versus right) was developed. This equation, which contained a linear independent combination of the two parameters, described the time-varying neural activity during the tasks. Based on the model, a regression approach was developed to decode the two parameters from the multiple-channel EEG signals. We demonstrated the continuous decoding of dynamic hand and speed information of the imagined clenching. In particular, the time-varying clenching speed was reconstructed in a bell-shaped profile. Our findings suggest an application to providing continuous and complex control of noninvasive brain-computer interface for movement-impaired paralytics.

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Year:  2010        PMID: 20168002      PMCID: PMC3036745          DOI: 10.1088/1741-2560/7/2/026001

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


  36 in total

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Authors:  Tao Wang; Jie Deng; Bin He
Journal:  Clin Neurophysiol       Date:  2004-12       Impact factor: 3.708

2.  Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis.

Authors:  Baharan Kamousi; Zhongming Liu; Bin He
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2005-06       Impact factor: 3.802

3.  Relationship between finger movement rate and functional magnetic resonance signal change in human primary motor cortex.

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4.  Coupling between neuronal firing, field potentials, and FMRI in human auditory cortex.

Authors:  Roy Mukamel; Hagar Gelbard; Amos Arieli; Uri Hasson; Itzhak Fried; Rafael Malach
Journal:  Science       Date:  2005-08-05       Impact factor: 47.728

5.  Primate motor cortex and free arm movements to visual targets in three-dimensional space. III. Positional gradients and population coding of movement direction from various movement origins.

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Journal:  J Neurosci       Date:  1988-08       Impact factor: 6.167

6.  Direct cortical representation of drawing.

Authors:  A B Schwartz
Journal:  Science       Date:  1994-07-22       Impact factor: 47.728

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Authors:  A P Georgopoulos; J F Kalaska; R Caminiti; J T Massey
Journal:  J Neurosci       Date:  1982-11       Impact factor: 6.167

8.  Oscillatory gamma-band (30-70 Hz) activity induced by a visual search task in humans.

Authors:  C Tallon-Baudry; O Bertrand; C Delpuech; J Permier
Journal:  J Neurosci       Date:  1997-01-15       Impact factor: 6.167

9.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans.

Authors:  Jonathan R Wolpaw; Dennis J McFarland
Journal:  Proc Natl Acad Sci U S A       Date:  2004-12-07       Impact factor: 11.205

10.  Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons.

Authors:  Q G Fu; D Flament; J D Coltz; T J Ebner
Journal:  J Neurophysiol       Date:  1995-02       Impact factor: 2.714

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  32 in total

1.  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

2.  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

3.  Hand tapping at mixed frequencies requires more motor cortex activity compared to single frequencies: an fNIRS study.

Authors:  Koen L M Koenraadt; Jacques Duysens; Bart M Meddeler; Noël L W Keijsers
Journal:  Exp Brain Res       Date:  2013-09-07       Impact factor: 1.972

Review 4.  Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives.

Authors:  Han Yuan; Bin He
Journal:  IEEE Trans Biomed Eng       Date:  2014-05       Impact factor: 4.538

5.  Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms.

Authors:  Bin He; Bryan Baxter; Bradley J Edelman; Christopher C Cline; Wendy Ye
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2015-05-20       Impact factor: 10.961

6.  Movement Anticipation and EEG: Implications for BCI-Contingent Robot Therapy.

Authors:  Sumner Norman; Mark Dennison; Eric Wolbrecht; Steven Cramer; Ramesh Srinivasan; David Reinkensmeyer
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2016-02-11       Impact factor: 3.802

7.  Assessing human mirror activity with EEG mu rhythm: A meta-analysis.

Authors:  Nathan A Fox; Marian J Bakermans-Kranenburg; Kathryn H Yoo; Lindsay C Bowman; Erin N Cannon; Ross E Vanderwert; Pier F Ferrari; Marinus H van IJzendoorn
Journal:  Psychol Bull       Date:  2015-12-21       Impact factor: 17.737

8.  Goal selection versus process control while learning to use a brain-computer interface.

Authors:  Audrey S Royer; Minn L Rose; Bin He
Journal:  J Neural Eng       Date:  2011-04-21       Impact factor: 5.379

9.  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

10.  Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain-computer interface to a virtual reality avatar.

Authors:  Trieu Phat Luu; Yongtian He; Samuel Brown; Sho Nakagame; Jose L Contreras-Vidal
Journal:  J Neural Eng       Date:  2016-04-11       Impact factor: 5.379

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