title: A new (semantic) reflexive brain-computer interface: in search for a suitable classifier.
abstract: The goal of the current study is to find a suitable classifier for electroencephalogram (EEG) data derived from a new learning paradigm which aims at communication in paralysis. A reflexive semantic classical (Pavlovian) conditioning paradigm is explored as an alternative to the operant learning paradigms, currently used in most brain-computer interfaces (BCIs). Comparable with a lie-detection experiment, subjects are presented with true and false statements. The EEG activity following true and false statements was classified with the aim to separate covert 'yes' from covert 'no' responses. Four classification algorithms are compared for classifying off-line data collected from a group of 14 healthy participants: (i) stepwise linear discriminant analysis (SWLDA), (ii) shrinkage linear discriminant analysis (SLDA), (iii) linear support vector machine (LIN-SVM) and (iv) radial basis function kernel support vector machine (RBF-SVM). The results indicate that all classifiers perform at chance level when separating conditioned 'yes' from conditioned 'no' responses. However, single conditioned reactions could be successfully classified on a single-trial basis (single conditioned reaction against a baseline interval). All of the four investigated classification methods achieve comparable performance, however results with RBF-SVM show the highest single-trial classification accuracy of 68.8%. The results suggest that the proposed paradigm may allow affirmative and negative (disapproving negative) communication in a BCI experiment.
authors.affiliation: Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany.
authors.forename: A
authors.initials: A
authors.lastname: Furdea
authors.forename: C A
authors.initials: CA
authors.lastname: Ruf
authors.forename: S
authors.initials: S
authors.lastname: Halder
authors.forename: D
authors.initials: D
authors.lastname: De Massari
authors.forename: M
authors.initials: M
authors.lastname: Bogdan
authors.forename: W
authors.initials: W
authors.lastname: Rosenstiel
authors.forename: T
authors.initials: T
authors.lastname: Matuz
authors.forename: N
authors.initials: N
authors.lastname: Birbaumer
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003214:Conditioning, Classical / Q000502:physiology*; D016002:Discriminant Analysis; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D012660:Semantics; D060388:Support Vector Machine; D014584:User-Computer Interface; D055815:Young Adult