Sparse-coded cross-domain adaptation from the visual to the brain domain
Brain decoding (i.e., retrieving information from brain signals by employing machine learning algorithms) has recently received considerable attention across many communities. In a typical brain decoding paradigm, different types of stimuli are shown to the participant of the neuroimaging experiment, while his/her concurrent brain activity is captured using neuroimaging techniques. Then a machine learning algorithm is employed to categorize the measured brain signal into the target stimuli classes. Accurate prediction of the stimulus category by the algorithm is considered a positive evidence of the hypothesis of the existence of stimulus-related information in brain data. However, most of the brain decoding studies suffer from the constraint of having few and noisy samples. In order to overcome this limitation, in this paper, an adaptation paradigm is employed in order to transfer knowledge from visual domain to brain domain. We experimentally show that such adaptation procedure leads to improved results for the object recognition task in the brain domain, outperforming significantly the results achieved by the brain features alone. This is the first study in the direction of transferring knowledge by adapting representations learned on visual domain to the brain modality. We believe this paper opens up avenues for exploiting large-scale visual datasets to achieve performance gain in brain decoding.