title: Detection of event-related potentials in individual subjects using support vector machines.
abstract: Event-related potentials (ERPs) are tiny electrical brain responses in the human electroencephalogram that are typically not detectable until they are isolated by a process of signal averaging. Owing to the extremely smallsize of ERP components (ranging from less than 1 μV to tens of μV), compared to background brain rhythms, statistical analyses of ERPs are predominantly carried out in groups of subjects. This limitation is a barrier to the translation of ERP-based neuroscience to applications such as medical diagnostics. We show here that support vector machines (SVMs) are a useful method to detect ERP components in individual subjects with a small set of electrodes and a small number of trials for a mismatch negativity (MMN) ERP component. Such a reduced experiment setup is important for clinical applications. One hundred healthy individuals were presented with an auditory pattern containing pattern-violating deviants to evoke the MMN. Two-class SVMs were then trained to classify averaged ERP waveforms in response to the standard tone (tones that match the pattern) and deviant tone stimuli (tones that violate the pattern). The influence of kernel type, number of epochs, electrode selection, and temporal window size in the averaged waveform were explored. When using all electrodes, averages of all available epochs, and a temporal window from 0 to 900-ms post-stimulus, a linear SVM achieved 94.5 % accuracy. Further analyses using SVMs trained with narrower, sliding temporal windows confirmed the sensitivity of the SVM to data in the latency range associated with the MMN.
authors.affiliation: Faculty of Computer Science, Dalhousie University, 6050 University Avenue, P.O. Box 1500, Halifax, NS, B3H 4R2, Canada.
authors.forename: Hossein
authors.initials: H
authors.lastname: Parvar
authors.affiliation: Biomedical Translational Imaging Centre (BIOTIC), IWK Health Centre, Halifax, NS, Canada.
authors.forename: Lauren
authors.initials: L
authors.lastname: Sculthorpe-Petley
authors.affiliation: School of Psychology, Faculty of Science, University of Nottingham Malaysia Campus, Semenyih, Selangor, Malaysia.
authors.forename: Jason
authors.initials: J
authors.lastname: Satel
authors.affiliation: Faculty of Computer Science, Dalhousie University, 6050 University Avenue, P.O. Box 1500, Halifax, NS, B3H 4R2, Canada.
authors.forename: Rober
authors.initials: R
authors.lastname: Boshra
authors.affiliation: Applied Sciences, Simon Fraser University, Surrey, BC, Canada.
authors.forename: Ryan C N
authors.initials: RC
authors.lastname: D'Arcy
authors.affiliation: Faculty of Computer Science, Dalhousie University, 6050 University Avenue, P.O. Box 1500, Halifax, NS, B3H 4R2, Canada. tt@cs.dal.ca.
authors.forename: Thomas P
authors.initials: TP
authors.lastname: Trappenberg
keywords: Diagnostic; Electroencephalography; Event-related potentials; Mismatch negativity; Neuroscience; Support vector machine