  How to effectively and efficiently extract valid and reliable features from
high-dimensional electroencephalography (EEG), particularly how to fuse the
spatial and temporal dynamic brain information into a better feature
representation, is a critical issue in brain data analysis. Most current EEG
studies are working on handcrafted features with a supervised modeling, which
would be limited by experience and human feedbacks to a great extent. In this
paper, we propose a practical hybrid unsupervised deep CNN-RNN-GAN based EEG
feature characterization and fusion model, which is termed as EEGFuseNet.
EEGFuseNet is trained in an unsupervised manner, and deep EEG features covering
spatial and temporal dynamics are automatically characterized. Comparing to the
handcrafted features, the deep EEG features could be considered to be more
generic and independent of any specific EEG task. The performance of the
extracted deep and low-dimensional features by EEGFuseNet is carefully
evaluated in an unsupervised emotion recognition application based on a famous
public emotion database. The results demonstrate the proposed EEGFuseNet is a
robust and reliable model, which is easy to train and manage and perform
efficiently in the representation and fusion of dynamic EEG features. In
particular, EEGFuseNet is established as an optimal unsupervised fusion model
with promising subject-based leave-one-out results in the recognition of four
emotion dimensions (valence, arousal, dominance and liking), which demonstrates
the possibility of realizing EEG based cross-subject emotion recognition in a
pure unsupervised manner.
