  To enhance the performance of affective models and reduce the cost of
acquiring physiological signals for real-world applications, we adopt
multimodal deep learning approach to construct affective models from multiple
physiological signals. For unimodal enhancement task, we indicate that the best
recognition accuracy of 82.11% on SEED dataset is achieved with shared
representations generated by Deep AutoEncoder (DAE) model. For multimodal
facilitation tasks, we demonstrate that the Bimodal Deep AutoEncoder (BDAE)
achieves the mean accuracies of 91.01% and 83.25% on SEED and DEAP datasets,
respectively, which are much superior to the state-of-the-art approaches. For
cross-modal learning task, our experimental results demonstrate that the mean
accuracy of 66.34% is achieved on SEED dataset through shared representations
generated by EEG-based DAE as training samples and shared representations
generated by eye-based DAE as testing sample, and vice versa.
