Literature DB >> 19641479

Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000.

J Adam Wilson1, Gerwin Schalk, Léo M Walton, Justin C Williams.   

Abstract

A brain-computer interface (BCI) functions by translating a neural signal, such as the electroencephalogram (EEG), into a signal that can be used to control a computer or other device. The amplitude of the EEG signals in selected frequency bins are measured and translated into a device command, in this case the horizontal and vertical velocity of a computer cursor. First, the EEG electrodes are applied to the user s scalp using a cap to record brain activity. Next, a calibration procedure is used to find the EEG electrodes and features that the user will learn to voluntarily modulate to use the BCI. In humans, the power in the mu (8-12 Hz) and beta (18-28 Hz) frequency bands decrease in amplitude during a real or imagined movement. These changes can be detected in the EEG in real-time, and used to control a BCI ([1],[2]). Therefore, during a screening test, the user is asked to make several different imagined movements with their hands and feet to determine the unique EEG features that change with the imagined movements. The results from this calibration will show the best channels to use, which are configured so that amplitude changes in the mu and beta frequency bands move the cursor either horizontally or vertically. In this experiment, the general purpose BCI system BCI2000 is used to control signal acquisition, signal processing, and feedback to the user [3].

Entities:  

Mesh:

Year:  2009        PMID: 19641479      PMCID: PMC2900251          DOI: 10.3791/1319

Source DB:  PubMed          Journal:  J Vis Exp        ISSN: 1940-087X            Impact factor:   1.355


  3 in total

1.  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

2.  Conversion of EEG activity into cursor movement by a brain-computer interface (BCI).

Authors:  Georg E Fabiani; Dennis J McFarland; Jonathan R Wolpaw; Gert Pfurtscheller
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2004-09       Impact factor: 3.802

3.  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

  3 in total
  14 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

2.  Signals from intraventricular depth electrodes can control a brain-computer interface.

Authors:  Jerry J Shih; Dean J Krusienski
Journal:  J Neurosci Methods       Date:  2011-10-21       Impact factor: 2.390

3.  Control of a brain-computer interface using stereotactic depth electrodes in and adjacent to the hippocampus.

Authors:  D J Krusienski; J J Shih
Journal:  J Neural Eng       Date:  2011-03-24       Impact factor: 5.379

4.  BCI-FES With Multimodal Feedback for Motor Recovery Poststroke.

Authors:  Alexander B Remsik; Peter L E van Kan; Shawna Gloe; Klevest Gjini; Leroy Williams; Veena Nair; Kristin Caldera; Justin C Williams; Vivek Prabhakaran
Journal:  Front Hum Neurosci       Date:  2022-07-06       Impact factor: 3.473

5.  Simultaneous scalp electroencephalography (EEG), electromyography (EMG), and whole-body segmental inertial recording for multi-modal neural decoding.

Authors:  Thomas C Bulea; Atilla Kilicarslan; Recep Ozdemir; William H Paloski; Jose L Contreras-Vidal
Journal:  J Vis Exp       Date:  2013-07-26       Impact factor: 1.355

6.  Dose-response relationships using brain-computer interface technology impact stroke rehabilitation.

Authors:  Brittany M Young; Zack Nigogosyan; Léo M Walton; Alexander Remsik; Jie Song; Veena A Nair; Mitchell E Tyler; Dorothy F Edwards; Kristin Caldera; Justin A Sattin; Justin C Williams; Vivek Prabhakaran
Journal:  Front Hum Neurosci       Date:  2015-06-23       Impact factor: 3.169

7.  Evaluation of EEG features in decoding individual finger movements from one hand.

Authors:  Ran Xiao; Lei Ding
Journal:  Comput Math Methods Med       Date:  2013-04-24       Impact factor: 2.238

8.  EEG resolutions in detecting and decoding finger movements from spectral analysis.

Authors:  Ran Xiao; Lei Ding
Journal:  Front Neurosci       Date:  2015-09-01       Impact factor: 4.677

9.  Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device.

Authors:  Brittany Mei Young; Zack Nigogosyan; Alexander Remsik; Léo M Walton; Jie Song; Veena A Nair; Scott W Grogan; Mitchell E Tyler; Dorothy Farrar Edwards; Kristin Caldera; Justin A Sattin; Justin C Williams; Vivek Prabhakaran
Journal:  Front Neuroeng       Date:  2014-07-08

10.  Case report: post-stroke interventional BCI rehabilitation in an individual with preexisting sensorineural disability.

Authors:  Brittany M Young; Zack Nigogosyan; Veena A Nair; Léo M Walton; Jie Song; Mitchell E Tyler; Dorothy F Edwards; Kristin Caldera; Justin A Sattin; Justin C Williams; Vivek Prabhakaran
Journal:  Front Neuroeng       Date:  2014-06-24
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