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Detection of event-related potentials in individual subjects using support vector machines

Citation

Parvar, H and Sculthorpe-Petley, L and Satel, J and Boshra, R and D'Arcy, RCN and Trappenberg, TP, Detection of event-related potentials in individual subjects using support vector machines, Brain Informatics, 2, (1) pp. 1-12. ISSN 2198-4018 (2015) [Refereed Article]


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Copyright Statement

Copyright The Author(s) 2014. Licensed under Creative Commons Attribution

DOI: doi:10.1007/s40708-014-0006-7

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 small size of ERP components (ranging from less than 1 lV to tens of lV), 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.

Item Details

Item Type:Refereed Article
Keywords:attention
Research Division:Psychology and Cognitive Sciences
Research Group:Cognitive Sciences
Research Field:Computer Perception, Memory and Attention
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Psychology and Cognitive Sciences
Author:Satel, J (Dr Jason Satel)
ID Code:111177
Year Published:2015
Deposited By:Psychology
Deposited On:2016-09-01
Last Modified:2016-10-10
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