--- a
+++ b/visualization/topography.py
@@ -0,0 +1,46 @@
+# plot EEG topograpy with mne 
+# https://mne.tools/stable/index.html
+
+import mne
+import numpy as np
+import scipy.io
+import matplotlib.pyplot as plt
+from matplotlib import mlab as mlab
+# from preprocess import import_data
+
+data = 0
+label = 0
+# get the data and label 
+# data - (samples, channels, trials)
+# label -  (label, 1)
+
+data = np.transpose(data, (2, 1, 0))
+label = np.squeeze(np.transpose(label))
+idx = np.where(label == 1)
+data_draw = data[idx]
+
+mean_trial = np.mean(data_draw, axis=0)  # mean trial
+# use standardization or normalization to adjust
+mean_trial = (mean_trial - np.mean(mean_trial)) / np.std(mean_trial)
+
+
+mean_ch = np.mean(mean_trial, axis=1)  # mean samples with channel dimension left
+
+
+# Draw topography
+biosemi_montage = mne.channels.make_standard_montage('biosemi64')  # set a montage, see mne document
+index = [37, 9, 10, 46, 45, 44, 13, 12, 11, 47, 48, 49, 50, 17, 18, 31, 55, 54, 19, 30, 56, 29]  # correspond channel
+biosemi_montage.ch_names = [biosemi_montage.ch_names[i] for i in index]
+biosemi_montage.dig = [biosemi_montage.dig[i+3] for i in index]
+info = mne.create_info(ch_names=biosemi_montage.ch_names, sfreq=250., ch_types='eeg')  # sample rate
+
+evoked1 = mne.EvokedArray(mean_trial, info)
+evoked1.set_montage(biosemi_montage)
+plt.figure(1)
+# im, cn = mne.viz.plot_topomap(np.mean(mean_trial, axis=1), evoked1.info, show=False)
+im, cn = mne.viz.plot_topomap(mean_ch, evoked1.info, show=False)
+plt.colorbar(im)
+
+plt.savefig('./topo/test.png')
+print('the end')
+