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


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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.HighlightsProof-of-principle of a ‘cognitive brain-computer-interface’ using realtime fMRI.Higher order visual brain regions used to decode the allocation of attention.Online single-block classification of 4-quadrant spatial attention to realworld images.Brain signal detection made more efficient by using ‘m-sequences’.Higher order mental processes produce more information for communication interfaces.

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