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MindsEyeBCI

Reading the mind’s eye at 7 Tesla - A fMRI-based communication brain-computer interface for severely motor-impaired patients

Total Cost €

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EC-Contrib. €

0

Partnership

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Project "MindsEyeBCI" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITEIT MAASTRICHT 

Organization address
address: Minderbroedersberg 4-6
city: MAASTRICHT
postcode: 6200 MD
website: http://www.maastrichtuniversity.nl

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Netherlands [NL]
 Total cost 148˙530 €
 EC max contribution 148˙530 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-PoC
 Funding Scheme ERC-POC
 Starting year 2018
 Duration (year-month-day) from 2018-01-01   to  2019-06-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITEIT MAASTRICHT NL (MAASTRICHT) coordinator 148˙530.00

Map

 Project objective

The Advanced ERC project ColumnarCodeCracking has pioneered ultra-high field fMRI at 7 Tesla for sub-millimeter neuroscience applications targeting cortical columns and cortical layers. One sub-project of the ERC project was to explore whether it is possible to build new brain computer interfaces (BCIs) exploiting the strong signal quality and higher resolution achievable at ultra-high magnetic fields. As part of our research (Emmerling et al., 2017, http://cordis.europa.eu/news/rcn/124885_en.html) we discovered that it is possible to reconstruct letter shapes from activity in early visual areas that are merely imagined by participants during 7 Tesla fMRI scanning. Importantly, we could demonstrate that imagined letter shapes can be decoded from single imagination events of about 10 seconds without the need to average across multiple repetitions. These observations stimulated the idea for this PoC application, namely to use letter imagery for the first time as a communication BCI. We also have tested deep learning auto-encoder networks as part of the analysis and observed that these tools substantially increase the robustness of letter reconstruction. The three major goals of this PoC are 1) to perform 7 Tesla fMRI experiments with healthy participants to evaluate whether decoding brain activity patterns during letter imagery can be performed robust enough to be used as a communication BCI for severely motor-impaired (locked-in) patients, 2) to develop a BCI/neurofeedback software performing all required advanced online analyses, and 3) evaluate whether showing the online decoded letter during imagery helps participants to fine-tune the resulting shape. While 7 Tesla fMRI is not yet widely available in clinical settings, we aim to prepare first tests of the developed prototype with locked-in patients.

 Publications

year authors and title journal last update
List of publications.
2019 Mario Senden, Thomas C. Emmerling, Rick van Hoof, Martin A. Frost, Rainer Goebel
Reconstructing imagined letters from early visual cortex reveals tight topographic correspondence between visual mental imagery and perception
published pages: 1167-1183, ISSN: 1863-2653, DOI: 10.1007/s00429-019-01828-6
Brain Structure and Function 224/3 2020-01-30

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The information about "MINDSEYEBCI" are provided by the European Opendata Portal: CORDIS opendata.

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