Fig. 11.

The learning curves for different memory units in different datasets. In the case of both datasets, GRU-RNN converge faster than LSTM-RNN. To reach lowest loss, GRU-RNN acquires less number of iterations than LSTM-RNN. For some specific subjects, GRU-RNN obtains lower average cross-entropy loss than LSTM-RNN within 200 iterations. Overall, the subjects’ EEG signals from “Dataset 2a” and “Dataset 2b” represent similar average cross-entropy between LSTM-RNN and GRU-RNN. a LSTM-RNN in “Dataset 2a”, b GRU-RNN in “Dataset 2a”, c LSTM-RNN in “Dataset 2b” and d GRU-RNN in “Dataset 2b”