diff --git a/examples/wiki-sleep-analysis.py b/examples/wiki-sleep-analysis.py
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+++ b/examples/wiki-sleep-analysis.py
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+# Data processing
+## Load packages
+import mne
+import yasa
+import numpy as np
+import matplotlib.pyplot as plt
+
+## Load data
+ch_names = ['EOG-above', 'EOG-canthus', 'EMG-chin', 'Dry-O2', 'Dry-C2', 'Dry-F2', 'Sticky-C1', 'Wet-Fc1']
+data = np.loadtxt("wiki-sleep-analysis-data/Mentalab-sleep-analysis_ExG.csv", skiprows=1, delimiter=',').transpose()[1:9]
+sf = 250
+ch_types = ["eog", "eog", "emg", "eeg", "eeg", "eeg", "eeg", "eeg"]
+info = mne.create_info(ch_names = ch_names, sfreq = sf, ch_types = ch_types)
+raw = mne.io.RawArray(data, info)
+raw.apply_function(lambda x: x * 1e-6) # scale muV to V
+
+## Pre-processing
+raw.filter(0.1, 40) # Apply a bandpass filter from 0.1 to 40 Hz
+
+# Detecting sleep stages
+sls = yasa.SleepStaging(raw, eeg_name="Sticky-C1")
+
+## Computing predicted labels
+y_pred = sls.predict()
+y_pred[0:40]
+
+## Computing the hypnogram
+hypno = yasa.Hypnogram(y_pred)
+hypno_int = yasa.hypno_str_to_int(y_pred)
+hypno_up = yasa.hypno_upsample_to_data(hypno=hypno_int, sf_hypno=(1/30), data=data, sf_data=sf)
+
+# Inspecting bandpower
+data_filt = raw.get_data() * 1e6
+data_c1_uV = data_filt[6]
+bandpower_stages = yasa.bandpower(data_c1_uV, sf = 100, win_sec=4, relative=True, hypno=hypno_up, include=(0, 1, 2, 3, 4))
+bandpower_avg = bandpower_stages.groupby('Stage')[['Delta', 'Theta', 'Alpha', 'Sigma', 'Beta', 'Gamma']].mean()
+bandpower_avg.index = ['Wake', 'N1', 'N2', 'N3', 'REM']
+
+print(bandpower_avg)
+
+# Spectrogram and hypnogram
+print(hypno_up.shape, 'Unique values =', np.unique(hypno_up))
+fig = yasa.plot_spectrogram(data_c1_uV, sf, hypno=hypno_up, fmax=30, cmap='Spectral_r', trimperc=5)
+fig.show()
+
+# Slow wave and sleep spindle detection
+## Slow waves
+sw = yasa.sw_detect(data_c1_uV, sf, hypno=hypno_up)
+sw.summary()
+
+sw.plot_average(time_before=0.4, time_after=0.8, center="NegPeak");
+plt.legend(['C1'])
+plt.show()
+
+## Sleep spindles
+sp = yasa.spindles_detect(data_c1_uV, sf)
+sp.summary()
+
+sp.plot_average(time_before=0.6, time_after=0.6);
+plt.legend(['C1'])
+plt.show()
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