Literature DB >> 18270004

Toward self-paced brain-computer communication: navigation through virtual worlds.

Reinhold Scherer1, Felix Lee, Alois Schlogl, Robert Leeb, Horst Bischof, Gert Pfurtscheller.   

Abstract

The self-paced control paradigm enables users to operate brain-computer interfaces (BCI) in a more natural way: no longer is the machine in control of the timing and speed of communication, but rather the user is. This is important to enhance the usability, flexibility, and response time of a BCI. In this work, we show how subjects, after performing cue-based feedback training (smiley paradigm), learned to navigate self-paced through the "freeSpace" virtual environment (VE). Similar to computer games, subjects had the task of picking up items by using the following navigation commands: rotate left, rotate right, and move forward ( three classes). Since the self-paced control paradigm allows subjects to make voluntary decisions on time, type, and duration of mental activity, no cues or routing directives were presented. The BCI was based only on three bipolar electroencephalogram channels and operated by motor imagery. Eye movements (electrooculogram) and electromyographic artifacts were reduced and detected online. The results of three able-bodied subjects are reported and problems emerging from self-paced control are discussed.

Entities:  

Mesh:

Year:  2008        PMID: 18270004     DOI: 10.1109/TBME.2007.903709

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  30 in total

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4.  Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges.

Authors:  J D R Millán; R Rupp; G R Müller-Putz; R Murray-Smith; C Giugliemma; M Tangermann; C Vidaurre; F Cincotti; A Kübler; R Leeb; C Neuper; K-R Müller; D Mattia
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6.  Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain-computer interface.

Authors:  Alexander J Doud; John P Lucas; Marc T Pisansky; Bin He
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7.  BCI Competition IV - Data Set I: Learning Discriminative Patterns for Self-Paced EEG-Based Motor Imagery Detection.

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Journal:  Front Neurosci       Date:  2012-02-06       Impact factor: 4.677

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

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9.  A binary method for simple and accurate two-dimensional cursor control from EEG with minimal subject training.

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10.  Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS.

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Journal:  PLoS One       Date:  2012-07-23       Impact factor: 3.240

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