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Fig. 8 | BMC Bioinformatics

Fig. 8

From: Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network

Fig. 8

The prediction results of LSTM-RNN and GRU-RNN by 20, 30, and 90 hidden layers on channel “ C3”. The deep RNN architecture will predict the same level of signals as the number of hidden layers increased. A highest number of hidden layers will get rich sequential relationships which have a similar spectrum to the EEG signals. a 20 hidden layers of LSTM unit, b 20 hidden layers of GRU, c 30 hidden layers of LSTM unit, d 30 hidden layers of GRU, e 90 hidden layers of LSTM unit and f 90 hidden layers of GRU

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