Name
Title | ||
|---|---|---|
EEG-based Endogenous Online Co-Adaptive Brain-Computer Interfaces: Strategy for Success? |
Abstract | ||
|---|---|---|
A Brain-Computer Interface (BCI) translates patterns of brain signals such as the electroencephalogram (EEG) into messages for communication and control. In the case of endogenous systems the reliable detection of induced patterns is more challenging than the detection of the more stable and stereotypical evoked responses. In the former case specific mental activities such as motor imagery are used to encode different messages. In the latter case users have to attend to sensory stimuli to evoke a characteristic response. Indeed, a large number of users who try to control endogenous BCIs do not reach sufficient level of accuracy. This fact is also known as BCI “inefficiency” or “illiteracy”. In this paper we discuss and make some conjectures, based on our knowledge and experience in BCI, on whether or not online co-adaptation of human and machine can be the solution to overcome this challenge. We point out some ingredients that might be necessary for the system to be reliable and allow the users to attain sufficient control. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CEEC.2018.8674198 | 2018 10th Computer Science and Electronic Engineering (CEEC) |
Keywords | DocType | ISSN |
Electroencephalography,Feature extraction,Training,Task analysis,Machine learning,Pattern recognition,Brain modeling | Conference | 2472-1530 |
ISBN | Citations | PageRank |
978-1-5386-7275-4 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Reinhold Scherer | 1 | 489 | 63.20 |
| Josef Faller | 2 | 32 | 3.46 |
| Paul Sajda | 3 | 651 | 89.86 |
| Carmen Vidaurre | 4 | 299 | 35.35 |