Explore the words cloud of the DecoMP_ECoG project. It provides you a very rough idea of what is the project "DecoMP_ECoG" about.
The following table provides information about the project.
Coordinator |
UNIVERSITY COLLEGE LONDON
Organization address contact info |
Coordinator Country | United Kingdom [UK] |
Project website | https://sites.google.com/site/decompecog/ |
Total cost | 241˙169 € |
EC max contribution | 241˙169 € (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-GF |
Starting year | 2015 |
Duration (year-month-day) | from 2015-07-01 to 2018-11-21 |
Take a look of project's partnership.
# | ||||
---|---|---|---|---|
1 | UNIVERSITY COLLEGE LONDON | UK (LONDON) | coordinator | 241˙169.00 |
2 | BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY | US (STANFORD) | partner | 0.00 |
Despite the critical importance of memory for cognitive function and socialization, very little is known about how information is stored for later retrieval and use. Understanding how the human brain maintains and stores information would enhance research on memory dysfunction in degenerative diseases, such as the age-related dementias, which represent a large burden for European society, and could facilitate the development of strategies for improving memory. The current proposal will use intracranial electrophysiological recordings from the surface of the human brain to investigate encoding, retrieval and consolidation of category-specific information during experimental settings, as well as during spontaneous brain activity. The proposal consists in two parts: first, electrocorticographic (ECoG) data will be acquired at Stanford University, with access to high-quality recordings and modern tools for electrophysiological data analysis. Secondly, machine learning based methodologies will be developed at the Department of Computer Science, University College London (return host) to decode spontaneous brain activity in different vigilance states. Finally, all developed methods will be implemented in an open source software, ensuring the timely dissemination of state-of-the art techniques. The methodological developments considered in this project could provide means for developing computer-aided diagnostic tools for neurodegenerative diseases.
year | authors and title | journal | last update |
---|---|---|---|
2018 |
Aaron Kucyi, Jessica Schrouff, Stephan Bickel, Brett L. Foster, James M. Shine, Josef Parvizi Intracranial electrophysiology reveals reproducible intrinsic functional connectivity within human brain networks published pages: 0217-18, ISSN: 0270-6474, DOI: 10.1523/JNEUROSCI.0217-18.2018 |
The Journal of Neuroscience | 2019-06-11 |
2018 |
Jessica Schrouff, J. M. Monteiro, L. Portugal, M. J. Rosa, C. Phillips, J. Mourão-Miranda Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models published pages: 117-143, ISSN: 1539-2791, DOI: 10.1007/s12021-017-9347-8 |
Neuroinformatics 16/1 | 2019-06-11 |
2016 |
Jessica Schrouff, Janaina Mourão-Miranda, Christophe Phillips, Josef Parvizi Decoding intracranial EEG data with multiple kernel learning method published pages: 19-28, ISSN: 0165-0270, DOI: 10.1016/j.jneumeth.2015.11.028 |
Journal of Neuroscience Methods 261 | 2019-06-11 |
2016 |
Amy L. Daitch, Brett L. Foster, Jessica Schrouff, Vinitha Rangarajan, Itır Kaşikçi, Sandra Gattas, Josef Parvizi Mapping human temporal and parietal neuronal population activity and functional coupling during mathematical cognition published pages: E7277-E7286, ISSN: 0027-8424, DOI: 10.1073/pnas.1608434113 |
Proceedings of the National Academy of Sciences 113/46 | 2019-06-11 |
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The information about "DECOMP_ECOG" are provided by the European Opendata Portal: CORDIS opendata.