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This work presents a novel method of exploring human brain-visualrepresentations, with a view towards replicating these processes in machines.The core idea is to learn plausible computational and biologicalrepresentations by correlating human neural activity and natural images. Thus,we first propose a model, EEG-ChannelNet, to learn a brain manifold for EEGclassification. After verifying that visual information can be extracted fromEEG data, we introduce a multimodal approach that uses deep image and EEGencoders, trained in a siamese configuration, for learning a joint manifoldthat maximizes a compatibility measure between visual features and brainrepresentations. We then carry out image classification and saliency detectionon the learned manifold. Performance analyses show that our approachsatisfactorily decodes visual information from neural signals. This, in turn,can be used to effectively supervise the training of deep learning models, asdemonstrated by the high performance of image classification and saliencydetection on out-of-training classes. The obtained results show that thelearned brain-visual features lead to improved performance and simultaneouslybring deep models more in line with cognitive neuroscience work related tovisual perception and attention.

Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features