--- a
+++ b/preprocess_h5_smt.py
@@ -0,0 +1,55 @@
+'''Preprocessor for KU Data.
+'''
+import argparse
+from os.path import join as pjoin
+
+import h5py
+import numpy as np
+from scipy.io import loadmat
+from scipy.signal import decimate
+from tqdm import tqdm
+
+parser = argparse.ArgumentParser(
+    description='Preprocessor for KU Data')
+parser.add_argument('source', type=str, help='Path to raw KU data')
+parser.add_argument('target', type=str, help='Path to pre-processed KU data')
+parser.add_argument('--foldered-data', action='store_true',
+                    help='DEPRECATED. DO NOT use this option ' +
+                    'if the data is downloaded from GigaDB.')
+args = parser.parse_args()
+
+src = args.source
+out = args.target
+is_foldered = args.foldered_data
+
+
+def get_data(sess, subj):
+    if is_foldered:
+        filename = pjoin('session' + str(sess), 's' + str(subj), 'EEG_MI.mat')
+    else:
+        filename = 'sess{:02d}_subj{:02d}_EEG_MI.mat'.format(sess, subj)
+    filepath = pjoin(src, filename)
+    raw = loadmat(filepath)
+    # Obtain input, convert (time, epoch, chan) into (epoch, chan, time)
+    X1 = np.moveaxis(raw['EEG_MI_train']['smt'][0][0], 0, -1)
+    X1 = decimate(X1, 4)
+    X2 = np.moveaxis(raw['EEG_MI_test']['smt'][0][0], 0, -1)
+    X2 = decimate(X2, 4)
+    X = np.concatenate((X1, X2), axis=0)
+    # Obtain target: 0 -> right, 1 -> left
+    Y1 = (raw['EEG_MI_train']['y_dec'][0][0][0] - 1)
+    Y2 = (raw['EEG_MI_test']['y_dec'][0][0][0] - 1)
+    Y = np.concatenate((Y1, Y2), axis=0)
+    return X, Y
+
+
+with h5py.File(pjoin(out, 'KU_mi_smt.h5'), 'w') as f:
+    for subj in tqdm(range(1, 55)):
+        X1, Y1 = get_data(1, subj)
+        X2, Y2 = get_data(2, subj)
+        X = np.concatenate((X1, X2), axis=0)
+        X = X.astype(np.float32)
+        Y = np.concatenate((Y1, Y2), axis=0)
+        Y = Y.astype(np.int64)
+        f.create_dataset('s' + str(subj) + '/X', data=X)
+        f.create_dataset('s' + str(subj) + '/Y', data=Y)