  The auditory attention decoding (AAD) approach was proposed to determine the
identity of the attended talker in a multi-talker scenario by analyzing
electroencephalography (EEG) data. Although the linear model-based method has
been widely used in AAD, the linear assumption was considered oversimplified
and the decoding accuracy remained lower for shorter decoding windows.
Recently, nonlinear models based on deep neural networks (DNN) have been
proposed to solve this problem. However, these models did not fully utilize
both the spatial and temporal features of EEG, and the interpretability of DNN
models was rarely investigated. In this paper, we proposed novel convolutional
recurrent neural network (CRNN) based regression model and classification
model, and compared them with both the linear model and the state-of-the-art
DNN models. Results showed that, our proposed CRNN-based classification model
outperformed others for shorter decoding windows (around 90% for 2 s and 5 s).
Although worse than classification models, the decoding accuracy of the
proposed CRNN-based regression model was about 5% greater than other regression
models. The interpretability of DNN models was also investigated by visualizing
layers' weight.
