Calculates temporal statistics for each microstate class from a segmented EEG recording: mean duration, occurrence rate, and time coverage. Also computes the transition probability matrix between states.
Arguments
- x
A PhysioExperiment object with microstate labels in
metadata(x)$microstates(fromeegMicrostates).
Value
A data.frame with columns:
- state
Integer microstate class (1 to n_states).
- duration_ms
Mean duration of consecutive runs in milliseconds.
- occurrence_per_sec
Number of state occurrences (runs) per second.
- coverage_pct
Percentage of total time spent in this state.
The transition probability matrix (n_states x n_states) is stored as
an attribute "transition_matrix".
References
Michel, C. M., & Koenig, T. (2018). EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks. NeuroImage, 180, 577-593.
Examples
pe <- make_eeg(n_time = 5000, n_channels = 19, sr = 500)
pe <- eegMicrostates(pe, n_states = 4, method = "kmeans")
stats <- eegMicrostateStats(pe)
print(stats)
#> state duration_ms occurrence_per_sec coverage_pct
#> 1 1 409.5000 0.4 16.38
#> 2 2 2682.0000 0.2 53.64
#> 3 3 313.6667 0.6 18.82
#> 4 4 558.0000 0.2 11.16
attr(stats, "transition_matrix")
#> [,1] [,2] [,3] [,4]
#> [1,] 0.0 0.25 0.75 0.0
#> [2,] 0.0 0.00 0.50 0.5
#> [3,] 0.8 0.00 0.00 0.2
#> [4,] 0.0 0.00 1.00 0.0