Objective
Interest in developing a new method of man-machine communication - the Brain-Computer Interface (BCI) - has grown steadily over the past few decades. A BCI is a communication system that implements the principle of ``think and make it happen without any p hysical effort'' by bypassing the conventional output pathways of nerves and muscles. BCI technology therefore can provide a new communication and control option for individuals that cannot otherwise express their wishes to the outside world. From a mach ine learning standpoint BCI presents a number of significant challenges. Firstly, the features produced by the brain monitoring system are multi-dimensional (10s sensors), rapidly sampled (100-1000s Hz), time series. Secondly, short training times imply that only a few examples (trials of the training session) are available. A third issue is the high level of noise in the signals from both non-neural and neural sources. A final challenge is the high variability of brain signals, due either to changes in the users non-BCI relevant brain states or users changing strategies to improve BCI control. I propose to investigate integrating adaptivity into the machine learning process. The objective here is to determine if integrating adaption with powerful clas sifiers is feasible and whether the potential benefits of adapting to the changing BCI environment are realisable. The project will be undertaken at a European centre of excellence for Machine learning and BCI, under the guidance of world leaders in the f ield. The training in machine learning and BCI that I will receive will be essential to a career in this emerging field.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors
- natural sciences computer and information sciences artificial intelligence machine learning
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
FP6-2004-MOBILITY-5
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Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
MUENCHEN
Germany
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.