  This work presents a novel method of exploring human brain-visual
representations, with a view towards replicating these processes in machines.
The core idea is to learn plausible computational and biological
representations by correlating 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 deep image and EEG
encoders, trained in a siamese configuration, for learning a joint manifold
that maximizes a compatibility measure between visual features and brain
representations. We then carry out image classification and saliency detection
on the learned manifold. Performance analyses show that our approach
satisfactorily decodes visual information from neural signals. This, in turn,
can be used to effectively supervise the training of deep learning models, as
demonstrated by the high performance of image classification and saliency
detection on out-of-training classes. The obtained results show that the
learned brain-visual features lead to improved performance and simultaneously
bring deep models more in line with cognitive neuroscience work related to
visual perception and attention.
