# Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Text

arXiv: https://arxiv.org/abs/2405.00726 · HTML: https://mlsanity.com/paper/2405.00726/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 122 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 14 of 14)

- [Mind Reader: Reconstructing complex images from brain activities](https://mlsanity.com/paper/2210.01769/) (2022)
- [Deep Learning Human Mind for Automated Visual Classification](https://mlsanity.com/paper/1609.00344/) (2016)
- [Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification](https://mlsanity.com/paper/2112.02690/) (2021)
- [ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation](https://mlsanity.com/paper/1912.00903/) (2019)
- [EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals](https://mlsanity.com/paper/1806.01875/) (2018)
- [Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks](https://mlsanity.com/paper/1511.06448/) (2015)
- [Transfer Learning in Brain-Computer Interfaces](https://mlsanity.com/paper/1512.00296/) (2015)
- [DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation](https://mlsanity.com/paper/2309.14030/) (2023)
- [Brain2Word: Decoding Brain Activity for Language Generation](https://mlsanity.com/paper/2009.04765/) (2020)
- [On The Effects Of Data Normalisation For Domain Adaptation On EEG Data](https://mlsanity.com/paper/2210.01081/) (2022)
- [Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals](https://mlsanity.com/paper/1709.08820/) (2017)
- [Effective and Extensible Feature Extraction Method Using Genetic Algorithm-Based Frequency-Domain Feature Search for Epileptic EEG Multi-classification](https://mlsanity.com/paper/1701.06120/) (2017)
- [A General Framework for Revealing Human Mind with auto-encoding GANs](https://mlsanity.com/paper/2102.05236/) (2021)
- [Ongoing EEG artifact correction using blind source separation](https://mlsanity.com/paper/2306.16910/) (2023)

## Built on by (most cited first, 10 of 10)

- [Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation](https://mlsanity.com/paper/2505.17099/) (2025)
- [Foundation Models for Cross-Domain EEG Analysis Application: A Survey](https://mlsanity.com/paper/2508.15716/) (2025)
- [MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification](https://mlsanity.com/paper/2512.01443/) (2025)
- [A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond](https://mlsanity.com/paper/2502.12048/) (2025)
- [ZUNA: Flexible EEG Superresolution with Position-Aware Diffusion Autoencoders](https://mlsanity.com/paper/2602.18478/) (2026)
- [How Many Bytes Can You Take Out Of Brain-To-Text Decoding?](https://mlsanity.com/paper/2405.14055/) (2024)
- [EEG-to-Text Translation: A Model for Deciphering Human Brain Activity](https://mlsanity.com/paper/2505.13936/) (2025)
- [LI-DSN: A Layer-wise Interactive Dual-Stream Network for EEG Decoding](https://mlsanity.com/paper/2604.01889/) (2026)
- [Structure-Guided Diffusion Model for EEG-Based Visual Cognition Reconstruction](https://mlsanity.com/paper/2604.22649/) (2026)
- [Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark](https://mlsanity.com/paper/2607.18749/) (2026)

## Abstract (arXiv)

The conversion of brain activity into text using electroencephalography (EEG) has gained significant traction in recent years. Many researchers are working to develop new models to decode EEG signals into text form. Although this area has shown promising developments, it still faces numerous challenges that necessitate further improvement. It's important to outline this area's recent developments and future research directions. In this review article, we thoroughly summarize the progress in EEG-to-text conversion. Firstly, we talk about how EEG-to-text technology has grown and what problems we still face. Secondly, we discuss existing techniques used in this field. This includes methods for collecting EEG data, the steps to process these signals, and the development of systems capable of translating these signals into coherent text. We conclude with potential future research directions, emphasizing the need for enhanced accuracy, reduced system constraints, and the exploration of novel applications across varied sectors. By addressing these aspects, this review aims to contribute to developing more accessible and effective Brain-Computer Interface (BCI) technology for a broader user base.