# Brain informed transfer learning for categorizing construction hazards

arXiv: https://arxiv.org/abs/2211.12420 · HTML: https://mlsanity.com/paper/2211.12420/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 117 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Incorporating Domain Knowledge into Deep Neural Networks](https://mlsanity.com/paper/2103.00180/) (2021)
- [A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples](https://mlsanity.com/paper/2112.01800/) (2021)
- [Using Human Brain Activity to Guide Machine Learning](https://mlsanity.com/paper/1703.05463/) (2017)
- [Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study](https://mlsanity.com/paper/2108.00107/) (2021)
- [Machine Learning Methods for Brain Network Classification: Application to Autism Diagnosis using Cortical Morphological Networks](https://mlsanity.com/paper/2004.13321/) (2020)
- [Brain-mediated Transfer Learning of Convolutional Neural Networks](https://mlsanity.com/paper/1905.10037/) (2019)
- [Seeing eye-to-eye? A comparison of object recognition performance in humans and deep convolutional neural networks under image manipulation](https://mlsanity.com/paper/2007.06294/) (2020)

## Built on by (most cited first, 1 of 1)

- [Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Driving](https://mlsanity.com/paper/2504.10296/) (2025)

## Abstract (arXiv)

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".