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  Real-time decoding of covert attention in higher-order visual areas

Ekanayake, J., Hutton, C., Ridgway, G., Scharnowski, F., Weiskopf, N., & Rees, G. (2018). Real-time decoding of covert attention in higher-order visual areas. NeuroImage, 169, 462-472. doi:10.1016/j.neuroimage.2017.12.019.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-20C9-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-12FD-C
Genre: Journal Article

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 Creators:
Ekanayake, Jinendra1, 2, 3, Author
Hutton, Chloe4, Author
Ridgway, Gerard5, Author
Scharnowski, Frank6, 7, 8, Author
Weiskopf, Nikolaus2, 9, Author              
Rees, Geraint2, 3, Author
Affiliations:
1Wellcome Trust Centre for Interventional and Surgical Sciences, University College London, United Kingdom, ou_persistent22              
2Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom, ou_persistent22              
3Institute of Cognitive Neuroscience, University College London, United Kingdom, ou_persistent22              
4Siemens Molecular Imaging, Oxford, United Kingdom, ou_persistent22              
5University of Oxford, United Kingdom, ou_persistent22              
6Psychiatric Hospital, University of Zurich, Switzerland, ou_persistent22              
7Neuroscience Center Zurich, University of Zurich, Switzerland, ou_persistent22              
8Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich, Switzerland, ou_persistent22              
9Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              

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 Abstract: Brain-computer-interfaces (BCI) provide a means of using human brain activations to control devices for communication. Until now this has only been demonstrated in primary motor and sensory brain regions, using surgical implants or non-invasive neuroimaging techniques. Here, we provide proof-of-principle for the use of higher-order brain regions involved in complex cognitive processes such as attention. Using realtime fMRI, we implemented an online 'winner-takes-all approach' with quadrant-specific parameter estimates, to achieve single-block classification of brain activations. These were linked to the covert allocation of attention to real-world images presented at 4-quadrant locations. Accuracies in three target regions were significantly above chance, with individual decoding accuracies reaching upto 70%. By utilising higher order mental processes, 'cognitive BCIs' access varied and therefore more versatile information, potentially providing a platform for communication in patients who are unable to speak or move due to brain injury.

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Language(s): eng - English
 Dates: 2017-12-062017-07-232017-12-092017-12-142018-04-01
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2017.12.019
PMID: 29247807
PMC: PMC5864512
Other: Epub 2017
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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 169 Sequence Number: - Start / End Page: 462 - 472 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166