Modeling uncertainties in EEG microstates: analysis of real and imagined motor movements using probabilistic clustering-driven training of probabilistic neural networks
File(s)
Published version
Author(s)
Dinov, M
Leech, R
Type
Journal Article
Abstract
Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analogue to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses.
Date Issued
2017-11-01
Date Acceptance
2017-10-20
Citation
Frontiers in Human Neuroscience, 2017, 11
ISSN
1662-5161
Publisher
Frontiers Media
Journal / Book Title
Frontiers in Human Neuroscience
Volume
11
Copyright Statement
© 2017 Dinov and Leech. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
License URL
Sponsor
Defence Science and Technology Laboratory (DSTL)
Grant Number
DSTLX-1000083275
Subjects
Science & Technology
Social Sciences
Life Sciences & Biomedicine
Neurosciences
Psychology
Neurosciences & Neurology
EEG
probabilistic microstates
K-Means
Fuzzy C-Means
multi-layer perceptrons
motor imagery
RESTING-STATE NETWORKS
BRAIN
DYNAMICS
SEGMENTATION
AVALANCHES
Experimental Psychology
Publication Status
Published
Article Number
534