title: Large-Scale Assessment of a Fully Automatic Co-Adaptive Motor Imagery-Based Brain Computer Interface.
abstract: In the last years Brain Computer Interface (BCI) technology has benefited from the development of sophisticated machine leaning methods that let the user operate the BCI after a few trials of calibration. One remarkable example is the recent development of co-adaptive techniques that proved to extend the use of BCIs also to people not able to achieve successful control with the standard BCI procedure. Especially for BCIs based on the modulation of the Sensorimotor Rhythm (SMR) these improvements are essential, since a not negligible percentage of users is unable to operate SMR-BCIs efficiently. In this study we evaluated for the first time a fully automatic co-adaptive BCI system on a large scale. A pool of 168 participants naive to BCIs operated the co-adaptive SMR-BCI in one single session. Different psychological interventions were performed prior the BCI session in order to investigate how motor coordination training and relaxation could influence BCI performance. A neurophysiological indicator based on the Power Spectral Density (PSD) was extracted by the recording of few minutes of resting state brain activity and tested as predictor of BCI performances. Results show that high accuracies in operating the BCI could be reached by the majority of the participants before the end of the session. BCI performances could be significantly predicted by the neurophysiological indicator, consolidating the validity of the model previously developed. Anyway, we still found about 22% of users with performance significantly lower than the threshold of efficient BCI control at the end of the session. Being the inter-subject variability still the major problem of BCI technology, we pointed out crucial issues for those who did not achieve sufficient control. Finally, we propose valid developments to move a step forward to the applicability of the promising co-adaptive methods.
authors.affiliation: Neurotechnology Group, Technische Universität Berlin, Berlin, Germany.
authors.forename: Laura
authors.initials: L
authors.lastname: Acqualagna
authors.affiliation: Institute of Psychology, University of Würzburg, Würzburg, Germany.
authors.forename: Loic
authors.initials: L
authors.lastname: Botrel
authors.affiliation: Machine Learning Group, Technische Universität Berlin, Berlin, Germany.
authors.forename: Carmen
authors.initials: C
authors.lastname: Vidaurre
authors.affiliation: Institute of Psychology, University of Würzburg, Würzburg, Germany.
authors.forename: Andrea
authors.initials: A
authors.lastname: Kübler
authors.affiliation: Institute of Psychology, University of Würzburg, Würzburg, Germany.|Bernstein Focus Neurotechnology, Berlin, Germany.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Blankertz
mesh_terms: D000293:Adolescent; D000328:Adult; D001676:Biofeedback, Psychology; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D015203:Reproducibility of Results; D055815:Young Adult