Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics

Abstract

The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.

Large-scale EEG neural network changes in response to therapeutic TMS

“…Microstate segmentation has drawn criticism as an overly simplistic measure of EEG dynamics. The "winner-take-all" labeling excludes the possibility of competing microstate classes during EEG segments and presumes discontinuous EEG evolution (54). Although these EEG features appear to reflect a clinically relevant measure, we were careful to interpret results of our microstate analysis within the constraints of its GEV (76.5%) and clustering methods, and the approach we used generated results accounting for a large amount of the variance in brain activity.…”

Tracking the Brain’s Intrinsic Connectivity Networks in EEG

“…One alternate approach is to track EEG microstates [17], which are spatial correlates of ICNs identified by spatial clustering. However, despite its increasing popularity in probing dysfunctional ICN dynamics in numerous psychological conditions [26], this analysis is riddled with flawed assumptions that lead to inaccuracies at finer temporal scales [43, 27].…”

Exploring differences for motor imagery using Teager energy operator-based EEG microstate analyses

“…Lehmann proved that the specific frequency band (8-12 Hz) of the EEG signals could be converted into discrete states, which are called "microstates", are defined by topographies of electric potentials recorded over the scalp, and that they can remain stable for 80-120 ms before rapidly transitioning to a different microstate [22,23]. Some recent articles also provided a critical view of the topography at any given time point is in one state and emphasized the continuous nature of the EEG dynamics that underlie microstate sequences [24,25]. So microstates obtained from EEG signals can be used in brain-computer interface systems (BCIs) to control external devices (e.g., robotic arms [26], intelligent wheelchairs [27] and other external equipment [28]).…”