TESANet: Self-attention network for olfactory EEG classification

The olfactory system is known to be associated with emotion during odor stimulation. A well-designed computational model that can correctly recognize preference induced by odor stimulation can be vital in the food and perfume industries. Electroencephalogram (EEG) can be used to study the brain's response to odor stimulation due to its good temporal resolution and low acquisition cost. In this study, we proposed a novel self-attention deep learning framework: Temporal Segment Attention Network (TESANet) to classify the brain state of subjects when they are exposed to pleasant and unpleasant odors. Odor stimulation is a continuous process, the temporal dynamics of the EEG signal should reflect the continuous changes of the brain responses to the given odor, thus we design the model to capture the intercorrelation between time segments of the EEG by utilizing the self-attention mechanism. TESANet consists of a filter-bank layer to extract spectral features, a spatial convolution layer to extract spatial features, a temporal segmentation layer to split the data into overlapping time windows, a Long Short-Term Memory (LSTM) layer to encode the temporal segments, a self-attention layer to decode the temporal dynamics by learning the intercorrelation between time segments, and finally a fully connected layer for classification. Experiments on an olfactory EEG dataset demonstrated that the proposed method outperforms other competing deep learning methods for odor pleasantness classification.