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

ACL ARR 2025 May Submission4562 Authors

Abstract:

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.

Paper Type: Long

Research Area: Linguistic theories, Cognitive Modeling and Psycholinguistics

Research Area Keywords: cognitive modeling

Languages Studied: English

Submission Number: 4562