title: Automated selection of brain regions for real-time fMRI brain-computer interfaces.
abstract: Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps.
  
  
  
  Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.
authors.affiliation: Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands. Maastricht Brain Imaging Center, 6229 ER Maastricht, The Netherlands.
authors.forename: Michael
authors.initials: M
authors.lastname: Lührs
authors.forename: Bettina
authors.initials: B
authors.lastname: Sorger
authors.forename: Rainer
authors.initials: R
authors.lastname: Goebel
authors.forename: Fabrizio
authors.initials: F
authors.lastname: Esposito
mesh_terms: D000328:Adult; D001931:Brain Mapping / Q000379:methods*; D062207:Brain-Computer Interfaces; D003199:Computer Systems; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D009415:Nerve Net / Q000502:physiology*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013647:Task Performance and Analysis; D000069558:Unsupervised Machine Learning; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*; D015443:Word Processing