Literature DB >> 12964454

Critical decision-speed and information transfer in the "Graz Brain-Computer Interface".

G Krausz1, R Scherer, G Korisek, G Pfurtscheller.   

Abstract

The "Graz Brain-Computer Interface (BCI)" transforms changes in oscillatory EEG activity into control signals for external devices and feedback. These changes are induced by various motor imageries performed by the user. For this study, 2 different types of motor imagery (movement of the right vs. left hand or both feet) were classified by processing 2 bipolar EEG-channels (derived at electrode positions C3 and C4). After a few sessions, within some weeks, 4 young paraplegic patients learned to control the BCI. In accordance with the participants, decision-speed (trial length) was varied and the information transfer rate (ITR) was calculated for each run. All experimental runs have been feedback-runs employing a simple computer-game-like paradigm. A falling ball had to be led into a randomly marked target halfway down the screen. The horizontal position was controlled by the BCI-output signal and the trial length was varied by the investigator across runs. The goal was to find values for trial length enabling a maximum ITR. Three out of 4 participants had good results after a few runs. Analysis of their last 2 experimental sessions, each containing between 10 and 16 runs, showed that the trial length can be reduced to values around 2 s to obtain the highest possible information transfer. Attainable ITRs were between 5 and 17 bit/min depending on the participant's performance and condition.

Entities:  

Mesh:

Year:  2003        PMID: 12964454     DOI: 10.1023/a:1024637331493

Source DB:  PubMed          Journal:  Appl Psychophysiol Biofeedback        ISSN: 1090-0586


  16 in total

1.  Study of discriminant analysis applied to motor imagery bipolar data.

Authors:  Carmen Vidaurre; Reinhold Scherer; Rafael Cabeza; Alois Schlögl; Gert Pfurtscheller
Journal:  Med Biol Eng Comput       Date:  2006-12-01       Impact factor: 2.602

2.  Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients?

Authors:  A Kübler; N Birbaumer
Journal:  Clin Neurophysiol       Date:  2008-09-27       Impact factor: 3.708

3.  Discrimination of left and right leg motor imagery for brain-computer interfaces.

Authors:  Peter Boord; Ashley Craig; Yvonne Tran; Hung Nguyen
Journal:  Med Biol Eng Comput       Date:  2010-02-09       Impact factor: 2.602

4.  ERD-based online brain-machine interfaces (BMI) in the context of neurorehabilitation: optimizing BMI learning and performance.

Authors:  Surjo R Soekadar; Matthias Witkowski; Jürgen Mellinger; Ander Ramos; Niels Birbaumer; Leonardo G Cohen
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2011-10       Impact factor: 3.802

5.  Should the parameters of a BCI translation algorithm be continually adapted?

Authors:  Dennis J McFarland; William A Sarnacki; Jonathan R Wolpaw
Journal:  J Neurosci Methods       Date:  2011-05-06       Impact factor: 2.390

6.  Temporal coding of brain patterns for direct limb control in humans.

Authors:  Gernot R Müller-Putz; Reinhold Scherer; Gert Pfurtscheller; Christa Neuper
Journal:  Front Neurosci       Date:  2010-06-18       Impact factor: 4.677

7.  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

8.  Steering a tractor by means of an EMG-based human-machine interface.

Authors:  Jaime Gomez-Gil; Israel San-Jose-Gonzalez; Luis Fernando Nicolas-Alonso; Sergio Alonso-Garcia
Journal:  Sensors (Basel)       Date:  2011-07-11       Impact factor: 3.576

9.  An electrocorticographic BCI using code-based VEP for control in video applications: a single-subject study.

Authors:  Christoph Kapeller; Kyousuke Kamada; Hiroshi Ogawa; Robert Prueckl; Josef Scharinger; Christoph Guger
Journal:  Front Syst Neurosci       Date:  2014-08-07

10.  Whatever works: a systematic user-centered training protocol to optimize brain-computer interfacing individually.

Authors:  Elisabeth V C Friedrich; Christa Neuper; Reinhold Scherer
Journal:  PLoS One       Date:  2013-09-23       Impact factor: 3.240

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