# Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment

arXiv: https://arxiv.org/abs/2508.07283 · HTML: https://mlsanity.com/paper/2508.07283/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 10 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Cognitive Bias in Decision-Making with LLMs](https://mlsanity.com/paper/2403.00811/) (2024)
- [Evaluating Cognitive Maps and Planning in Large Language Models with CogEval](https://mlsanity.com/paper/2309.15129/) (2023)
- [Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice](https://mlsanity.com/paper/2405.19313/) (2024)
- [Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern LLMs](https://mlsanity.com/paper/2309.10371/) (2023)
- [On the Unexpected Abilities of Large Language Models](https://mlsanity.com/paper/2308.09720/) (2023)
- [An Evaluation of the EEG alpha-to-theta and theta-to-alpha band Ratios as Indexes of Mental Workload](https://mlsanity.com/paper/2202.12937/) (2022)

## Abstract (arXiv)

This study explores the intersection of electroencephalography (EEG) microstates and Large Language Models (LLMs) to enhance the assessment of cognitive load states. By utilizing EEG microstate features, the research aims to fine-tune LLMs for improved predictions of distinct cognitive states, specifically 'Rest' and 'Load'. The experimental design is delineated in four comprehensive stages: dataset collection and preprocessing, microstate segmentation and EEG backfitting, feature extraction paired with prompt engineering, and meticulous LLM model selection and refinement. Employing a supervised learning paradigm, the LLM is trained to identify cognitive load states based on EEG microstate features integrated into prompts, producing accurate discrimination of cognitive load. A curated dataset, linking EEG features to specified cognitive load conditions, underpins the experimental framework. The results indicate a significant improvement in model performance following the proposed fine-tuning, showcasing the potential of EEG-informed LLMs in cognitive neuroscience and cognitive AI applications. This approach not only contributes to the understanding of brain dynamics but also paves the way for advancements in machine learning techniques applicable to cognitive load and cognitive AI research.