# NeuroAnalyzer tutorials: Stationarity

There are several types of stationarity tests available in NeuroAnalyzer.

To test mean stationarity:

``````s_m = stationarity(eeg, method=:mean, window=256)
p = plot(s_m[1, :, 1], xlabel="time-window points", label="channel 1, epoch 1")
plot_save(p, file_name="images/eeg_s_m.png")`````` To test variance stationarity:

``````s_v = stationarity(eeg, method=:var, window=256)
p = plot(s_v[1, :, 1], xlabel="time-window points", label="channel 1, epoch 1")
plot_save(p, file_name="images/eeg_s_v.png")`````` To test phase stationarity using Hilbert transformation:

``````s_p = stationarity(eeg, method=:hilbert, window=256)
p = plot(eeg.epoch_time[1:end-1], s_p[1, :, 1], xlabel="epoch time [s]", label="channel 1, epoch 1", legend=:topright)
plot_save(p, file_name="images/eeg_s_p.png")`````` To test covariance stationarity based on Euclidean distance between covariance matrix of adjacent time windows:

``````s_c = stationarity(eeg, method=:cov, window=256)
p = plot(s_c[:, 15], label="epoch 15", ylabel="distance between covariance matrices", xlabel="time-window segment", legend=:topright)
plot_save(p, file_name="images/eeg_s_c.png")`````` To test phase stationarity using Augmented Dickey–Fuller test:

``````s_adf = stationarity(eeg, method=:adf, window=256)