Abstract
Brain decoding is able to make human interact with an external machine or robot for assisting patient's rehabilitation. Brain generic object recognition ability can be decoded through multiple neuroimaging modalities like functional magnetic resonance imaging (fMRI). On the other hand, external machine may wrongly recognize objects due to distorted noisy or blurring images caused by many factors, and therefore deteriorate performance of brain-machine interaction. In order to create better machine, generalization capability of human brain is transferred to classifier for enhancing classification accuracy of distorted images. Since homology existing between human and machine vision has been demonstrated, through decoding neural activity features of fMRI signals into feature units of convolutional neural network layers, an enhanced object recognition method is proposed to integrate brain activity into classifier for increasing classification accuracy. Experimental results show that the proposed method is able to enhance generalization capability of distorted object recognition.