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Originally applied to alpha oscillations in the 1970s, microstate (MS) analysis has since been used to decompose mainly broadband electroencephalogram (EEG) signals (e.g., 1–40 Hz). We hypothesised 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 first filtered EEG recordings into four classical frequency bands (delta, theta, alpha and beta) and thereafter compared their individual MS segmentations using mutual information as well as traditional MS measures (e.g., mean duration and time coverage). Firstly, we confirmed that MS topographies were spatially equivalent across all frequencies, matching the canonical broadband maps (A, B, C, D and C′). Interestingly, however, we observed strong informational independence of MS temporal sequences between spectral bands, together with significant divergence in traditional MS measures. For example, relative to broadband, alpha/beta band dynamics displayed greater time coverage of maps A and B, while map D was more prevalent in delta/theta bands. Moreover, using a frequency‐specific MS taxonomy (e.g., ϴA and αC), we were able to predict the eyes‐open versus eyes‐closed behavioural state significantly better using alpha‐band MS features compared with broadband ones (80 vs. 73% accuracy). Overall, our findings demonstrate the value and validity of spectrally specific MS analyses, which may prove useful for identifying new neural mechanisms in fundamental research and/or for biomarker discovery in clinical populations.

Beyond broadband: Towards a spectral decomposition of electroencephalography microstates