# Neural Memory Decoding with EEG Data and Representation Learning

arXiv: https://arxiv.org/abs/2307.13181 · HTML: https://mlsanity.com/paper/2307.13181/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 97 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Uncovering the structure of clinical EEG signals with self-supervised learning](https://mlsanity.com/paper/2007.16104/) (2020)
- [MAEEG: Masked Auto-encoder for EEG Representation Learning](https://mlsanity.com/paper/2211.02625/) (2022)
- [Decoding EEG Brain Activity for Multi-Modal Natural Language Processing](https://mlsanity.com/paper/2102.08655/) (2021)
- [Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals](https://mlsanity.com/paper/1607.03502/) (2016)
- [Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism](https://mlsanity.com/paper/2111.13208/) (2021)

## Abstract (arXiv)

We describe a method for the neural decoding of memory from EEG data. Using this method, a concept being recalled can be identified from an EEG trace with an average top-1 accuracy of about 78.4% (chance 4%). The method employs deep representation learning with supervised contrastive loss to map an EEG recording of brain activity to a low-dimensional space. Because representation learning is used, concepts can be identified even if they do not appear in the training data set. However, reference EEG data must exist for each such concept. We also show an application of the method to the problem of information retrieval. In neural information retrieval, EEG data is captured while a user recalls the contents of a document, and a list of links to predicted documents is produced.