Before revision:  The core idea is to learn plausible representations through the combined use of human neural activity evoked by natural imagesas a supervision mechanism for deep learning models. To accomplish this, we propose a multimodal approach that uses two different deep encoders, one for images and one for EEGs, trained in a siamese configuration  for learning a joint manifold that maximizes a compatibility measure between visual features and brain representation.
After revision:  The core idea is to learn plausible representations through the combined use of human neural activity and natural images. Thus, we first propose a model, EEG-ChannelNet, to learn a brain manifold for EEG classification. After verifying that visual information can be extracted from EEG data, we introduce a multimodal approach that uses two different deep encoders, one for images and one for EEGs, trained in a siamese configuration for learning a joint manifold that maximizes a compatibility measure between visual features and brain representation.