# Achieving More Human Brain-Like Vision via Human EEG Representational Alignment

arXiv: https://arxiv.org/abs/2401.17231 · HTML: https://mlsanity.com/paper/2401.17231/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 76 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 19 of 19)

- [Partial success in closing the gap between human and machine vision](https://mlsanity.com/paper/2106.07411/) (2021)
- [Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathway](https://mlsanity.com/paper/1411.6422/) (2014)
- [Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs](https://mlsanity.com/paper/1909.06161/) (2019)
- [Decoding Natural Images from EEG for Object Recognition](https://mlsanity.com/paper/2308.13234/) (2023)
- [Deep Learning Human Mind for Automated Visual Classification](https://mlsanity.com/paper/1609.00344/) (2016)
- [Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features](https://mlsanity.com/paper/2210.06756/) (2022)
- [Recurrence is required to capture the representational dynamics of the human visual system](https://mlsanity.com/paper/1903.05946/) (2019)
- [Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features](https://mlsanity.com/paper/1810.10974/) (2018)
- [The neuroconnectionist research programme](https://mlsanity.com/paper/2209.03718/) (2022)
- [Learning From Brains How to Regularize Machines](https://mlsanity.com/paper/1911.05072/) (2019)
- [Towards robust vision by multi-task learning on monkey visual cortex](https://mlsanity.com/paper/2107.14344/) (2021)
- [Recurrent computations for visual pattern completion](https://mlsanity.com/paper/1706.02240/) (2017)
- [Using Human Brain Activity to Guide Machine Learning](https://mlsanity.com/paper/1703.05463/) (2017)
- [A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains](https://mlsanity.com/paper/2310.13849/) (2023)
- [Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data](https://mlsanity.com/paper/1606.02840/) (2016)
- [End-to-end topographic networks as models of cortical map formation and human visual behaviour: moving beyond convolutions](https://mlsanity.com/paper/2308.09431/) (2023)
- [Approaching human 3D shape perception with neurally mappable models](https://mlsanity.com/paper/2308.11300/) (2023)
- [Improving the Accuracy and Robustness of CNNs Using a Deep CCA Neural Data Regularizer](https://mlsanity.com/paper/2209.02582/) (2022)
- [Generate your neural signals from mine: individual-to-individual EEG converters](https://mlsanity.com/paper/2304.10736/) (2023)

## Built on by (most cited first, 4 of 4)

- [Deep Models, Shallow Alignment: Uncovering the Granularity Mismatch in Neural Decoding](https://mlsanity.com/paper/2601.21948/) (2026)
- [Shifting Attention to You: Personalized Brain-Inspired AI Models](https://mlsanity.com/paper/2502.04658/) (2025)
- [Language learning shapes visual category-selectivity in deep neural networks](https://mlsanity.com/paper/2502.16456/) (2025)
- [The Visual Target Matters: Learning across the Visual Hierarchy for Brain-to-Image Retrieval](https://mlsanity.com/paper/2609.24136/) (2026)

## Abstract (arXiv)

Despite advancements in artificial intelligence, object recognition models still lag behind in emulating visual information processing in human brains. Recent studies have highlighted the potential of using neural data to mimic brain processing; however, these often rely on invasive neural recordings from non-human subjects, leaving a critical gap in understanding human visual perception. Addressing this gap, we present, 'Re(presentational)Al(ignment)net', a vision model aligned with human brain activity based on non-invasive EEG, demonstrating a significantly higher similarity to human brain representations. Our innovative image-to-brain multi-layer encoding framework advances human neural alignment by optimizing multiple model layers and enabling the model to efficiently learn and mimic the human brain's visual representational patterns across object categories and different modalities. Our findings suggest that ReAlnets better align artificial neural networks with human brain representations, making it more similar to human brain processing than traditional computer vision models, which takes an important step toward bridging the gap between artificial and human vision and achieving more brain-like artificial intelligence systems.