# Neural Spelling: A Spell-Based BCI System for Language Neural Decoding

arXiv: https://arxiv.org/abs/2501.17489 · HTML: https://mlsanity.com/paper/2501.17489/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 82 references that are in the map.
- First posted on arXiv 29 Jan 2025.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models](https://mlsanity.com/paper/2311.07919/) (2023)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis](https://mlsanity.com/paper/2304.04675/) (2023)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting](https://mlsanity.com/paper/2309.15649/) (2023)
- [Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification](https://mlsanity.com/paper/2112.02690/) (2021)
- [RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting](https://mlsanity.com/paper/2305.15685/) (2023)
- [EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces](https://mlsanity.com/paper/2006.00622/) (2020)
- [Can Generative Large Language Models Perform ASR Error Correction?](https://mlsanity.com/paper/2307.04172/) (2023)
- [EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces](https://mlsanity.com/paper/2405.00719/) (2024)
- [GrammarGPT: Exploring Open-Source LLMs for Native Chinese Grammatical Error Correction with Supervised Fine-Tuning](https://mlsanity.com/paper/2307.13923/) (2023)
- [Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition](https://mlsanity.com/paper/2409.09785/) (2024)
- [Speech Recognition with no speech or with noisy speech](https://mlsanity.com/paper/1903.00739/) (2019)
- [Efficient Eye Typing with 9-direction Gaze Estimation](https://mlsanity.com/paper/1707.00548/) (2017)
- [Using Large Language Model for End-to-End Chinese ASR and NER](https://mlsanity.com/paper/2401.11382/) (2024)
- [Large Language Model Should Understand Pinyin for Chinese ASR Error Correction](https://mlsanity.com/paper/2409.13262/) (2024)
- [NeuroAiR: Deep Learning Framework for Airwriting Recognition from Scalp-recorded Neural Signals](https://mlsanity.com/paper/2308.03555/) (2023)

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

- [Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies](https://mlsanity.com/paper/2510.22095/) (2025)
- [Foundation Models for Cross-Domain EEG Analysis Application: A Survey](https://mlsanity.com/paper/2508.15716/) (2025)
- [A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond](https://mlsanity.com/paper/2502.12048/) (2025)
- [BrainStack: Neuro-MoE with Functionally Guided Expert Routing for EEG-Based Language Decoding](https://mlsanity.com/paper/2601.21148/) (2026)

## Abstract (arXiv)

Brain-computer interfaces (BCIs) present a promising avenue by translating neural activity directly into text, eliminating the need for physical actions. However, existing non-invasive BCI systems have not successfully covered the entire alphabet, limiting their practicality. In this paper, we propose a novel non-invasive EEG-based BCI system with Curriculum-based Neural Spelling Framework, which recognizes all 26 alphabet letters by decoding neural signals associated with handwriting first, and then apply a Generative AI (GenAI) to enhance spell-based neural language decoding tasks. Our approach combines the ease of handwriting with the accessibility of EEG technology, utilizing advanced neural decoding algorithms and pre-trained large language models (LLMs) to translate EEG patterns into text with high accuracy. This system show how GenAI can improve the performance of typical spelling-based neural language decoding task, and addresses the limitations of previous methods, offering a scalable and user-friendly solution for individuals with communication impairments, thereby enhancing inclusive communication options.