Cross-domain MLP and CNN Transfer Learning for Biological Signal Processing: EEG and EMG

In this work, we show the success of unsupervised transfer learning between Electroencephalographic (brainwave) classification and Electromyographic (muscular wave) domains with both MLP
and CNN methods. To achieve this, signals are measured from both the brain and forearm muscles and EMG
data is gathered from a 4-class gesture classification experiment via the Myo Armband, and a 3-class mental
state EEG dataset is acquired via the Muse EEG Headband. A hyperheuristic multi-objective evolutionary
search method is used to find the best network hyperparameters. We then use this optimised topology
of deep neural network to classify both EMG and EEG signals, attaining results of 84.76% and 62.37%
accuracy, respectively. Next, when pre-trained weights from the EMG classification model are used for
initial distribution rather than random weight initialisation for EEG classification, 93.82%(+29.95) accuracy
is reached. When EEG pre-trained weights are used for initial weight distribution for EMG, 85.12% (+0.36)
accuracy is achieved. When the EMG network attempts to classify EEG, it outperforms the EEG network
even without any training (+30.25% to 82.39% at epoch 0), and similarly the EEG network attempting
to classify EMG data outperforms the EMG network (+2.38% at epoch 0). All transfer networks achieve
higher pre-training abilities, curves, and asymptotes, indicating that knowledge transfer is possible between
the two signal domains. In a second experiment with CNN transfer learning, the same datasets are projected
as 2D images and the same learning process is carried out. In the CNN experiment, EMG to EEG transfer
learning is found to be successful but not vice-versa, although EEG to EMG transfer learning did exhibit
a higher starting classification accuracy. The significance of this work is due to the successful transfer of
ability between models trained on two different biological signal domains, reducing the need for building
more computationally complex models in future research.