  What if we could effectively read the mind and transfer human visual
capabilities to computer vision methods? In this paper, we aim at addressing
this question by developing the first visual object classifier driven by human
brain signals. In particular, we employ EEG data evoked by visual object
stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative
brain activity manifold of visual categories. Afterwards, we train a
Convolutional Neural Network (CNN)-based regressor to project images onto the
learned manifold, thus effectively allowing machines to employ human
brain-based features for automated visual classification. We use a 32-channel
EEG to record brain activity of seven subjects while looking at images of 40
ImageNet object classes. The proposed RNN based approach for discriminating
object classes using brain signals reaches an average accuracy of about 40%,
which outperforms existing methods attempting to learn EEG visual object
representations. As for automated object categorization, our human brain-driven
approach obtains competitive performance, comparable to those achieved by
powerful CNN models, both on ImageNet and CalTech 101, thus demonstrating its
classification and generalization capabilities. This gives us a real hope that,
indeed, human mind can be read and transferred to machines.
