Affiliations: | 1997-2005 | | University of Lorraine |
| | 2008-2009 | | University of Bremen, Germany, Bremen, Bremen, Germany |
| | 2010-2010 | | CNRS Grenoble |
| | 2011-2013 | | University of California, Santa Barbara, Santa Barbara, CA, United States |
| | 2014-2017 | | Ulster University |
| | 2017- | | California State University, Fresno, Fresno, CA, United States |
Area:
Brain Computer Interfaces
We are testing a new system for linking grants to scientists.
The funding information displayed below comes from the
NIH Research Portfolio Online Reporting Tools and the
NSF Award Database.
The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
You can help! If you notice any innacuracies, please
sign in and mark grants as correct or incorrect matches.
Sign in to see low-probability grants and correct any errors in linkage between grants and researchers.
High-probability grants
According to our matching algorithm, Hubert Cecotti is the likely recipient of the following grants.
| Years |
Recipients |
Code |
Title / Keywords |
Matching
score |
| 2021 |
Cecotti, Hubert Charles |
R15Activity Code Description:
Supports small-scale research projects at educational institutions that provide baccalaureate or advanced degrees for a significant number of the Nation’s research scientists but that have not been major recipients of NIH support. The goals of the program are to (1) support meritorious research, (2) expose students to research, and (3) strengthen the research environment of the institution. Awards provide limited Direct Costs, plus applicable F&A costs, for periods not to exceed 36 months. This activity code uses multi-year funding authority; however, OER approval is NOT needed prior to an IC using this activity code. |
Brain-Computer Interface in Dynamic Tasks With Deep Learning and Functional Connectivity Analysis @ California State University Fresno
Abstract The PI proposes a high-impact multi-disciplinary research project to develop and validate machine learning al- gorithms for shift-detection in electroencephalogram (EEG) signals with applications to brain-computer interface to make them more reliable. Brain-computer interface is a means of communication for severely disabled peo- ple by decoding brain responses and translating their detection into commands with applications such a virtual keyboard or robotic control systems. Current brain-computer interface systems cannot be ef?ciently deployed in clinical setting due to their inability to properly take into account the non-stationarity properties of the evoked brain responses in the electroencephalogram signal. This project aims at enhancing the brain decoding perfor- mance when the task changes over time. The PI proposes to investigate the effects of well de?ned types of data shifts: covariate shift, probability shift, and concept shift to enhance brain decoding performance in changing tasks. The goals of this proposal are: 1) to characterize in event related potential (ERP) components neural signatures corresponding to task changes by using EEG recordings and machine learning techniques for single- trial detection. 2) to research in functional brain connectivity neural signature corresponding to task changes by using EEG recordings and directed model-based and model free techniques of functional brain connectivity. 3) to combine and adapt machine learning techniques to detect when changes occur during a task. This proposal will signi?cantly improve the infrastructure of research and education at California State University Fresno, Hispanic- Serving Institution and an Asian American and Native American Paci?c Islander-Serving Institution, introducing biomedical engineering research experiences to underrepresented minority and female students in computer science and psychology students. This would allow them to experience different stages of the scienti?c method, and acquire fundamental skills related to data science applied to physiological signals with potential impact on society for improving the life of severely disabled people.
|
0.925 |