  A transfer learning paradigm is proposed for "knowledge" transfer between the
human brain and convolutional neural network (CNN) for a construction hazard
categorization task. Participants' brain activities are recorded using
electroencephalogram (EEG) measurements when viewing the same images (target
dataset) as the CNN. The CNN is pretrained on the EEG data and then fine-tuned
on the construction scene images. The results reveal that the EEG-pretrained
CNN achieves a 9 % higher accuracy compared with a network with same
architecture but randomly initialized parameters on a three-class
classification task. Brain activity from the left frontal cortex exhibits the
highest performance gains, thus indicating high-level cognitive processing
during hazard recognition. This work is a step toward improving machine
learning algorithms by learning from human-brain signals recorded via a
commercially available brain-computer interface. More generalized visual
recognition systems can be effectively developed based on this approach of
"keep human in the loop".
