# LLMs Help Alleviate the Cross-Subject Variability in Brain Signal and Language Alignment

arXiv: https://arxiv.org/abs/2501.02621 · HTML: https://mlsanity.com/paper/2501.02621/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 5 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Machine learning for neural decoding](https://mlsanity.com/paper/1708.00909/) (2017)
- [Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning](https://mlsanity.com/paper/2203.02573/) (2022)
- [BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decoding](https://mlsanity.com/paper/2302.12971/) (2023)
- [Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders](https://mlsanity.com/paper/1812.06857/) (2018)
- [A 128 channel Extreme Learning Machine based Neural Decoder for Brain Machine Interfaces](https://mlsanity.com/paper/1509.07450/) (2015)
- [Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis](https://mlsanity.com/paper/2409.13532/) (2024)
- [BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision](https://mlsanity.com/paper/2309.12056/) (2023)
- [Towards an End-to-End Framework for Invasive Brain Signal Decoding with Large Language Models](https://mlsanity.com/paper/2406.11568/) (2024)
- [Effective and Efficient Intracortical Brain Signal Decoding with Spiking Neural Networks](https://mlsanity.com/paper/2412.20714/) (2024)

## 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)
- [Bridging Brain with Foundation Models through Self-Supervised Learning](https://mlsanity.com/paper/2506.16009/) (2025)
- [Large language models in medical time series analysis](https://mlsanity.com/paper/2609.22262/) (2026)

## Abstract (arXiv)

Decoding human activity from EEG signals has long been a popular research topic. While recent studies have increasingly shifted focus from single-subject to cross-subject analysis, few have explored the model's ability to perform zero-shot predictions on EEG signals from previously unseen subjects. This research aims to investigate whether deep learning methods can capture subject-independent semantic information inherent in human EEG signals. Such insights are crucial for Brain-Computer Interfaces (BCI) because, on one hand, they demonstrate the model's robustness against subject-specific temporal biases, and on the other, they significantly enhance the generalizability of downstream tasks. We employ Large Language Models (LLMs) as denoising agents to extract subject-independent semantic features from noisy EEG signals. Experimental results, including ablation studies, highlight the pivotal role of LLMs in decoding subject-independent semantic information from noisy EEG data. We hope our findings will contribute to advancing BCI research and assist both academia and industry in applying EEG signals to a broader range of applications.