Explore the words cloud of the MINDS project. It provides you a very rough idea of what is the project "MINDS" about.
The following table provides information about the project.
Coordinator |
DANMARKS TEKNISKE UNIVERSITET
Organization address contact info |
Coordinator Country | Denmark [DK] |
Project website | http://people.compute.dtu.dk/alvmu/minds.html |
Total cost | 212˙194 € |
EC max contribution | 212˙194 € (100%) |
Programme |
1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility) |
Code Call | H2020-MSCA-IF-2014 |
Funding Scheme | MSCA-IF-EF-ST |
Starting year | 2016 |
Duration (year-month-day) | from 2016-01-11 to 2018-01-10 |
Take a look of project's partnership.
# | ||||
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1 | DANMARKS TEKNISKE UNIVERSITET | DK (KGS LYNGBY) | coordinator | 212˙194.00 |
Functional magnetic resonance imaging (fMRI) is the dominating approach to research in the mapping of neural activity in the human brain. State of the art data analysis techniques employ a statistical parametric mapping (SPM) strategy to convert raw signal into interpretable images by processing data in a pipeline of task-specific modules. This approach, despite its simplicity and reliability, presents a set of inconveniences, including low interconnectivity among modules, resulting in suboptimal solutions. In this project we aim at making a major contribution to the field by replacing the step-by-step data processing pipeline by a deep neural network. We hypothesise that this will achieve better solutions by propagating the effects of module-based decisions through the network, jointly optimizing the whole processing pipeline. Moreover, fMRI low temporal resolution will be alleviated by means of a post-processing treatment, where advanced interpolation techniques will be used. We will release a freely accessible software tool that integrates with SPM, supplying an easy-to-use framework including advanced techniques for an automatic multivariate non-linear data analysis. The generated deep network solution will be applied in a multidisciplinary study in neurofeedback, where subjects will learn relaxation strategies guided by fMRI technology. At the end of the project, we expect our tool to become a useful standard practise in the field.
year | authors and title | journal | last update |
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2016 |
Albert Vilamala, Kristoffer H. Madsen, Lars K. Hansen Towards end-to-end optimisation of functional image analysis pipelines. published pages: , ISSN: , DOI: |
2019-06-18 | |
2017 |
Albert Vilamala, Kristoffer H. Madsen, Lars K. Hansen EEG Biofeedback for Relaxation using Deep Neural Networks. published pages: , ISSN: , DOI: |
2019-06-18 |
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The information about "MINDS" are provided by the European Opendata Portal: CORDIS opendata.