# Rethinking Cross-Subject Data Splitting for Brain-to-Text Decoding

arXiv: https://arxiv.org/abs/2312.10987 · HTML: https://mlsanity.com/paper/2312.10987/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification](https://mlsanity.com/paper/2112.02690/) (2021)
- [BOLD5000: A public fMRI dataset of 5000 images](https://mlsanity.com/paper/1809.01281/) (2018)
- [UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language](https://mlsanity.com/paper/2307.05355/) (2023)

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

- [What Are We Actually Decoding? Source Attribution for Non-Invasive Brain-to-Language Retrieval](https://mlsanity.com/paper/2605.24524/) (2026)
- [Language Reconstruction with Brain Predictive Coding from fMRI Data](https://mlsanity.com/paper/2405.11597/) (2024)
- [BioFormer: Rethinking Cross-Subject Generalization via Spectral Structural Alignment in Biomedical Time-Series](https://mlsanity.com/paper/2605.22468/) (2026)

## Abstract (arXiv)

Recent major milestones have successfully reconstructed natural language from non-invasive brain signals (e.g. functional Magnetic Resonance Imaging (fMRI) and Electroencephalogram (EEG)) across subjects. However, we find current dataset splitting strategies for cross-subject brain-to-text decoding are wrong. Specifically, we first demonstrate that all current splitting methods suffer from data leakage problem, which refers to the leakage of validation and test data into training set, resulting in significant overfitting and overestimation of decoding models. In this study, we develop a right cross-subject data splitting criterion without data leakage for decoding fMRI and EEG signal to text. Some SOTA brain-to-text decoding models are re-evaluated correctly with the proposed criterion for further research.