Abstract
The purpose of this study was to introduce an improved tool for automated classification of event-related potentials (ERPs) using spatiotemporally parcellated events incorporated into a functional brain network activation (BNA) analysis. The auditory oddball ERP paradigm was selected to demonstrate and evaluate the improved tool.Methods: The ERPs of each subject were decomposed into major dynamic spatiotemporal events. Then, a set of spatiotemporal events representing the group was generated by aligning and clustering the spatiotemporal events of all individual subjects. The temporal relationship between the common group events generated a network, which is the spatiotemporal reference BNA model. Scores were derived by comparing each subject's spatiotemporal events to the reference BNA model and were then entered into a support vector machine classifier to classify subjects into relevant subgroups. The reliability of the BNA scores (test-retest repeatability using intraclass correlation) and their utility as a classification tool were examined in the context of Target-Novel classification.Results: BNA intraclass correlation values of repeatability ranged between 0.51 and 0.82 for the known ERP components N100, P200, and P300. Classification accuracy was high when the trained data were validated on the same subjects for different visits (AUCs 0.93 and 0.95). The classification accuracy remained high for a test group recorded at a different clinical center with a different recording system (AUCs 0.81, 0.85 for 2 visits).Conclusion: The improved spatiotemporal BNA analysis demonstrates high classification accuracy. The BNA analysis method holds promise as a tool for diagnosis, follow-up and drug development associated with different neurological conditions.
Highlights
The high dimensional and complex nature of electroencephalogram (EEG) recordings is a result of the spatiotemporal structure of the neurophysiological signals
The ability to correctly classify minute and consistent changes in the event-related potential (ERP) waveform would suggest that the brain network activation (BNA) algorithm may have merit as a tool for diagnosis, follow-up and drug development associated with different neurological conditions
The evolution of the spatiotemporal parceled events (STEPs) over time for each stimulus condition is displayed for three frequency bands: δ (0.5–4 Hz; top row), θ (3–8 Hz; middle row) and α (7–13 Hz; bottom row), respectively
Summary
The high dimensional and complex nature of electroencephalogram (EEG) recordings is a result of the spatiotemporal structure of the neurophysiological signals. The entire spatiotemporal space involves a large quantity of data, not all of which are clinically significant, and necessitates a reduction in dimensions. Existing data reduction methods sometimes rely on a restriction of the solution space either in the temporal or spatial dimension and as a consequence, a large quantity of the spatiotemporal dynamics may be lost (Hasson-Meir et al, 2011). One known method of data-driven analysis is microstate analysis (Lehmann and Skrandies, 1980; Lehmann, 1987) This analysis assumes that a task-related brain activation can be segmented into specific functional states known as microstates, which are stable for about 80–120 ms, and which are each represented by the entire recording space at a specific time point (topographic map). Each state lacks the dynamic spatiotemporal evolution of the electric field at the scalp (Dimitriadis et al, 2013; Zoltowski et al, 2014; Khanna et al, 2015; Mheich et al, 2015)
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