TY - GEN
T1 - EEG-based Endogenous Online Co-Adaptive Brain-Computer Interfaces
T2 - 10th Computer Science and Electronic Engineering Conference, CEEC 2018
AU - Scherer, Reinhold
AU - Faller, Josef
AU - Sajda, Paul
AU - Vidaurre, Carmen
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/3/25
Y1 - 2019/3/25
N2 - 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.
AB - 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.
KW - Brain-Computer Interface (BCI)
KW - Electroencephalogram (EEG)
KW - Online Co-Adaptation
UR - https://www.scopus.com/pages/publications/85064389722
U2 - 10.1109/CEEC.2018.8674198
DO - 10.1109/CEEC.2018.8674198
M3 - Conference contribution
AN - SCOPUS:85064389722
T3 - 2018 10th Computer Science and Electronic Engineering Conference, CEEC 2018 - Proceedings
SP - 299
EP - 304
BT - 2018 10th Computer Science and Electronic Engineering Conference, CEEC 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 September 2018 through 21 September 2018
ER -