title: Comparison of adaptive features with linear discriminant classifier for Brain computer Interfaces.
abstract: Many Brain-computer Interfaces (BCI) use band-power estimates with more or less subject-specific optimization of the frequency bands. However, a number of alternative EEG features do not need to select the frequency bands; estimators for these features have been modified for an adaptive use. The popular band power estimates were compared with Adaptive AutoRegressive parameters, Hjorth, Barlow, Wackermann, Brain-Rate and a new feature type called Time Domain Parameter. The results from 21 subjects show that several features provide an equally good or even better performance, while no subject-specific optimization is needed, and they are also preferable to band power when the most discriminating frequency band of a subject is not known.
authors.affiliation: Intelligent Data Analysis Group, FIRST, Fraunhofer Institute, Kekulestr. 7, Berlin 12489, Germany. carmen.vidaurreATfirst.fraunhofer.de
authors.forename: Carmen
authors.initials: C
authors.lastname: Vidaurre
authors.forename: Alois
authors.initials: A
authors.lastname: Schlögl
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D016014:Linear Models; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface