Beyond broadband: Towards a spectral decomposition of EEG microstates

Microstate (MS) analysis takes advantage of the electroencephalogram’s (EEG) high temporal resolution to segment the brain’s electrical potentials into a temporal sequence of scalp topographies. Originally applied to alpha oscillations in the 1970s, MS analysis has since been used to decompose mainly broadband EEG signals (e.g. 1-40 Hz). We hypothesized that MS decomposition within separate, narrow frequency bands could provide more fine-grained information for capturing the spatio-temporal complexity of multichannel EEG. In this study using a large open-access dataset (n=203), we pre-filtered EEG recordings into 4 classical frequency bands (delta, theta, alpha, beta) in order to compare their individual MS segmentations using mutual information as well as traditional MS measures. Firstly, we confirmed that MS topographies were spatially equivalent across all frequencies, matching the canonical broadband maps (A, B, C, and D). Interestingly however, we observed strong informational independence of MS temporal sequences between spectral bands, together with significant divergence in traditional MS measures (e.g. mean duration, time coverage). For instance, MS sequences in the alpha-band exhibited temporal independence from sequences in all other frequencies, whilst also being longer on average (>100 ms). Based on a frequency vs. map taxonomy (e.g. ϴA, αC, βB), narrow-band MS analyses revealed novel relationships that were not evident from the coarse-grained broadband analysis. Overall, our findings demonstrate the value and validity of spectral MS analysis for decomposing the full-band EEG into a richer palette of frequency-specific microstates. This could prove useful for identifying new neural mechanisms in fundamental research and/or for biomarker discovery in clinical populations.