(IEEE, 2018) Scherer, Reinhold; Faller, Josef; Sajda, Paul; Vidaurre Arbizu, Carmen; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
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 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.
