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.

Major depression is one of the leading causes of disabling condition worldwide and its treatment is often challenging and unsatisfactory, since many patients become refractory to pharmacological therapies. Transcranial magnetic stimulation (TMS) is a noninvasive neurophysiological investigation mainly used to study the integrity of the primary motor cortex excitability and of the cortico-spinal tract. The development of paired-pulse and repetitive TMS (rTMS) paradigms has allowed investigators to explore the pathophysiology of depressive disorders and other neuropsychiatric diseases linked to brain excitability dysfunctions. Repetitive transcranial magnetic stimulation has also therapeutic and rehabilitative capabilities since it is able to induce changes in the excitability of inhibitory and excitatory neuronal networks that may persist in time. However, the therapeutic effects of rTMS on major depression have been demonstrated by analyzing only the improvement of neuropsychological performance. The aim of this study was to investigate cortical excitability changes on 12 chronically-medicated depressed patients (test group) after rTMS treatment and to correlate neurophysiological findings to neuropsychological outcomes. In detail, we assessed different parameters of cortical excitability before and after active rTMS in the test group, then compared to those of 10 age-matched depressed patients (control group) who underwent sham rTMS. In line with previous studies, at baseline both groups exhibited a significant interhemispheric difference of motor cortex excitability. This neurophysiological imbalance was then reduced in the patients treated with active rTMS, resulting also in a clinical benefit as demonstrated by the improvement in neuropsychological test scores. On the contrary, after sham rTMS, the interhemispheric difference was still evident in the control group. The reported clinical benefits in the test group might be related to the plastic remodeling of synaptic connection induced by rTMS treatment.