# Generate your neural signals from mine: individual-to-individual EEG converters

arXiv: https://arxiv.org/abs/2304.10736 · HTML: https://mlsanity.com/paper/2304.10736/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [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)
- [Recurrence is required to capture the representational dynamics of the human visual system](https://mlsanity.com/paper/1903.05946/) (2019)
- [Phase Imaging with Computational Specificity (PICS) for measuring dry mass changes in sub-cellular compartments](https://mlsanity.com/paper/2002.08361/) (2020)
- [Personalized visual encoding model construction with small data](https://mlsanity.com/paper/2202.02245/) (2022)
- [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)
- [Pyrcca: regularized kernel canonical correlation analysis in Python and its applications to neuroimaging](https://mlsanity.com/paper/1503.01538/) (2015)

## Built on by (most cited first, 3 of 3)

- [Achieving More Human Brain-Like Vision via Human EEG Representational Alignment](https://mlsanity.com/paper/2401.17231/) (2024)
- [Teaching CORnet Human fMRI Representations for Enhanced Model-Brain Alignment](https://mlsanity.com/paper/2407.10414/) (2024)
- [Language learning shapes visual category-selectivity in deep neural networks](https://mlsanity.com/paper/2502.16456/) (2025)

## Abstract (arXiv)

Most models in cognitive and computational neuroscience trained on one subject do not generalize to other subjects due to individual differences. An ideal individual-to-individual neural converter is expected to generate real neural signals of one subject from those of another one, which can overcome the problem of individual differences for cognitive and computational models. In this study, we propose a novel individual-to-individual EEG converter, called EEG2EEG, inspired by generative models in computer vision. We applied THINGS EEG2 dataset to train and test 72 independent EEG2EEG models corresponding to 72 pairs across 9 subjects. Our results demonstrate that EEG2EEG is able to effectively learn the mapping of neural representations in EEG signals from one subject to another and achieve high conversion performance. Additionally, the generated EEG signals contain clearer representations of visual information than that can be obtained from real data. This method establishes a novel and state-of-the-art framework for neural conversion of EEG signals, which can realize a flexible and high-performance mapping from individual to individual and provide insight for both neural engineering and cognitive neuroscience.