--- a
+++ b/Models/Network/LSTM.py
@@ -0,0 +1,46 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Import useful packages
+import tensorflow as tf
+
+
+def LSTM(Input, max_time, n_input, lstm_size, keep_prob, weights_1, biases_1, weights_2, biases_2):
+    '''
+
+    Args:
+        Input: The reshaped input EEG signals
+        max_time: The unfolded time slice of LSTM Model
+        n_input: The input signal size at one time
+        rnn_size: The number of LSTM units inside the LSTM Model
+        keep_prob: The Keep probability of Dropout
+        weights_1: The Weights of first fully-connected layer
+        biases_1: The biases of first fully-connected layer
+        weights_2: The Weights of second fully-connected layer
+        biases_2: The biases of second fully-connected layer
+
+    Returns:
+        FC_2: Final prediction of LSTM Model
+        FC_1: Extracted features from the first fully connected layer
+
+    '''
+
+    # One layer RNN Model
+    Input = tf.reshape(Input, [-1, max_time, n_input])
+    cell_encoder = tf.contrib.rnn.BasicLSTMCell(num_units=lstm_size)
+    encoder_drop = tf.contrib.rnn.DropoutWrapper(cell=cell_encoder, input_keep_prob=keep_prob)
+    outputs_encoder, final_state_encoder = tf.nn.dynamic_rnn(cell=encoder_drop, inputs=Input, dtype=tf.float32)
+
+    # First fully-connected layer
+    # final_state_encoder[0] is the long-term memory
+    FC_1 = tf.matmul(final_state_encoder[0], weights_1) + biases_1
+    FC_1 = tf.layers.batch_normalization(FC_1, training=True)
+    FC_1 = tf.nn.softplus(FC_1)
+    FC_1 = tf.nn.dropout(FC_1, keep_prob)
+
+    # Second fully-connected layer
+    FC_2 = tf.matmul(FC_1, weights_2) + biases_2
+    FC_2 = tf.nn.softmax(FC_2)
+
+    return FC_2, FC_1
+