title: Bring mental activity into action! An enhanced online co-adaptive brain-computer interface training protocol.
abstract: Non-stationarity and inherent variability of the noninvasive electroencephalogram (EEG) makes robust recognition of spontaneous EEG patterns challenging. Reliable modulation of EEG patterns that a BCI can robustly detect is a skill that users must learn. In this paper, we present a novel online co-adaptive BCI training paradigm. The system autonomously screens users for their ability to modulate EEG patterns in a predictive way and adapts its model parameters online. Results of a supporting study in seven first-time BCI users with disability are very encouraging. Three of 7 users achieved online accuracy > 70% for 2-class BCI control after 24 minutes of training. Online performance in 6 of 7 users was significantly higher than chance level. Online control was based on one single bipolar EEG channel. Beta band activity carried most discriminant information. Our fully automatic co-adaptive online approach allows to evaluate whether user benefit from current BCI technology within a reasonable timescale.
authors.forename: Reinhold
authors.initials: R
authors.lastname: Scherer
authors.forename: Josef
authors.initials: J
authors.lastname: Faller
authors.forename: Eloy
authors.initials: E
authors.lastname: Opisso
authors.forename: Ursula
authors.initials: U
authors.lastname: Costa
authors.forename: David
authors.initials: D
authors.lastname: Steyrl
authors.forename: Gernot R
authors.initials: GR
authors.lastname: Muller-Putz
mesh_terms: D000328:Adult; D000368:Aged; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009862:Online Systems; D013647:Task Performance and Analysis