Literature DB >> 20083864

An independent brain-computer interface using covert non-spatial visual selective attention.

Dan Zhang1, Alexander Maye, Xiaorong Gao, Bo Hong, Andreas K Engel, Shangkai Gao.   

Abstract

In this paper, a novel independent brain-computer interface (BCI) system based on covert non-spatial visual selective attention of two superimposed illusory surfaces is described. Perception of two superimposed surfaces was induced by two sets of dots with different colors rotating in opposite directions. The surfaces flickered at different frequencies and elicited distinguishable steady-state visual evoked potentials (SSVEPs) over parietal and occipital areas of the brain. By selectively attending to one of the two surfaces, the SSVEP amplitude at the corresponding frequency was enhanced. An online BCI system utilizing the attentional modulation of SSVEP was implemented and a 3-day online training program with healthy subjects was carried out. The study was conducted with Chinese subjects at Tsinghua University, and German subjects at University Medical Center Hamburg-Eppendorf (UKE) using identical stimulation software and equivalent technical setup. A general improvement of control accuracy with training was observed in 8 out of 18 subjects. An averaged online classification accuracy of 72.6 +/- 16.1% was achieved on the last training day. The system renders SSVEP-based BCI paradigms possible for paralyzed patients with substantial head or ocular motor impairments by employing covert attention shifts instead of changing gaze direction.

Entities:  

Mesh:

Year:  2010        PMID: 20083864     DOI: 10.1088/1741-2560/7/1/016010

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


  26 in total

Review 1.  Brain computer interfaces, a review.

Authors:  Luis Fernando Nicolas-Alonso; Jaime Gomez-Gil
Journal:  Sensors (Basel)       Date:  2012-01-31       Impact factor: 3.576

2.  High-speed spelling with a noninvasive brain-computer interface.

Authors:  Xiaogang Chen; Yijun Wang; Masaki Nakanishi; Xiaorong Gao; Tzyy-Ping Jung; Shangkai Gao
Journal:  Proc Natl Acad Sci U S A       Date:  2015-10-19       Impact factor: 11.205

3.  Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb.

Authors:  Petar Horki; Teodoro Solis-Escalante; Christa Neuper; Gernot Müller-Putz
Journal:  Med Biol Eng Comput       Date:  2011-03-11       Impact factor: 2.602

Review 4.  Guidelines for Feature Matching Assessment of Brain-Computer Interfaces for Augmentative and Alternative Communication.

Authors:  Kevin M Pitt; Jonathan S Brumberg
Journal:  Am J Speech Lang Pathol       Date:  2018-08-06       Impact factor: 2.408

5.  (C)overt attention and visual speller design in an ERP-based brain-computer interface.

Authors:  Matthias S Treder; Benjamin Blankertz
Journal:  Behav Brain Funct       Date:  2010-05-28       Impact factor: 3.759

6.  An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli.

Authors:  N J Hill; B Schölkopf
Journal:  J Neural Eng       Date:  2012-02-15       Impact factor: 5.379

7.  Examining sensory ability, feature matching and assessment-based adaptation for a brain-computer interface using the steady-state visually evoked potential.

Authors:  Jonathan S Brumberg; Anh Nguyen; Kevin M Pitt; Sean D Lorenz
Journal:  Disabil Rehabil Assist Technol       Date:  2018-01-31

8.  Implicit Neurofeedback Training of Feature-Based Attention Promotes Biased Sensory Processing during Integrative Decision-Making.

Authors:  Angela I Renton; David R Painter; Jason B Mattingley
Journal:  J Neurosci       Date:  2021-08-12       Impact factor: 6.167

9.  Performance assessment in brain-computer interface-based augmentative and alternative communication.

Authors:  David E Thompson; Stefanie Blain-Moraes; Jane E Huggins
Journal:  Biomed Eng Online       Date:  2013-05-16       Impact factor: 2.819

10.  Online adaptation of a c-VEP Brain-computer Interface(BCI) based on error-related potentials and unsupervised learning.

Authors:  Martin Spüler; Wolfgang Rosenstiel; Martin Bogdan
Journal:  PLoS One       Date:  2012-12-07       Impact factor: 3.240

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