title: Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials.
abstract: We present a novel approach to the problem of event-related potential (ERP) identification, based on a competitive artificial neural network (ANN) structure. Our method uses ensembled electroencephalogram (EEG) data just as used in conventional averaging, however without the need for a priori data subgrouping into distinct categories (e.g., stimulus- or event-related), and thus avoids conventional assumptions on response invariability. The competitive ANN, often described as a winner takes all neural structure, is based on dynamic competition among the net neurons where learning takes place only with the winning neuron. Using a simple single-layered structure, the proposed scheme results in convergence of the actual neural weights to the embedded ERP patterns. The method is applied to real event-related potential data recorded during a common odd-ball type paradigm. For the first time, within-session variable signal patterns are automatically identified, dismissing the strong and limiting requirement of a priori stimulus-related selective grouping of the recorded data. The results present new possibilities in ERP research.
authors.affiliation: Department of Electrical Engineering, Technion University, IIT Haifa, Israel. lange@rdmed.com
authors.forename: D H
authors.initials: DH
authors.lastname: Lange
authors.forename: H T
authors.initials: HT
authors.lastname: Siegelmann
authors.forename: H
authors.initials: H
authors.lastname: Pratt
authors.forename: G F
authors.initials: GF
authors.lastname: Inbar
mesh_terms: D016477:Artifacts; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer