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A rapid event-related potential (ERP) method for point-of-care evaluation of brain function: development of the Halifax Consciousness Scanner

Citation

Sculthorpe-Petley, L and Liu, C and Hajra, SG and Parvar, H and Satel, J and Trappenberg, TP and Boshra, R and D'Arcy, RC, A rapid event-related potential (ERP) method for point-of-care evaluation of brain function: development of the Halifax Consciousness Scanner, Journal of neuroscience methods, 245 pp. 64-72. ISSN 0165-0270 (2015) [Refereed Article]

Copyright Statement

Copyright 2015 Crown Copyright

DOI: doi:10.1016/j.jneumeth.2015.02.008

Abstract

Background: Event-related potentials (ERPs) may provide a non-invasive index of brain function for a range of clinical applications. However, as a lab-based technique, ERPs are limited by technical challenges that prevent full integration into clinical settings.

New method: To translate ERP capabilities from the lab to clinical applications, we have developed methods like the Halifax Consciousness Scanner (HCS). HCS is essentially a rapid, automated ERP evaluation of brain functional status. The present study describes the ERP components evoked from auditory tones and speech stimuli. ERP results were obtained using a 5-min test in 100 healthy individuals. The HCS sequence was designed to evoke the N100, the mismatch negativity (MMN), P300, the early negative enhancement (ENE), and the N400. These components reflected sensation, perception, attention, memory, and language perception, respectively. Component detection was examined at group and individual levels, and evaluated across both statistical and classification approaches.

Results: All ERP components were robustly detected at the group level. At the individual level, nonparametric statistical analyses showed reduced accuracy relative to support vector (SVM) machine classification, particularly for speech-based ERPs. Optimized SVM results were MMN: 95.6%; P300: 99.0%; ENE: 91.8%; and N400: 92.3%.

Conclusions: A spectrum of individual-level ERPs can be obtained in a very short time. Machine learning classification improved detection accuracy across a large healthy control sample. Translating ERPs into clinical applications is increasingly possible at the individual level.

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:111179
Year Published:2015
Web of Science® Times Cited:3
Deposited By:Psychology
Deposited On:2016-09-01
Last Modified:2017-10-31
Downloads:0

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