# EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation

arXiv: https://arxiv.org/abs/2401.18006 · HTML: https://mlsanity.com/paper/2401.18006/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://mlsanity.com/paper/2112.00114/) (2021)
- [What Can Transformers Learn In-Context? A Case Study of Simple Function Classes](https://mlsanity.com/paper/2208.01066/) (2022)
- [MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action](https://mlsanity.com/paper/2303.11381/) (2023)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data](https://mlsanity.com/paper/2101.12037/) (2021)
- [Hypothesis Search: Inductive Reasoning with Language Models](https://mlsanity.com/paper/2309.05660/) (2023)
- [Large Language Models are Few-Shot Health Learners](https://mlsanity.com/paper/2305.15525/) (2023)
- [ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning](https://mlsanity.com/paper/2304.06588/) (2023)
- [ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification](https://mlsanity.com/paper/1802.00308/) (2018)
- [CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models](https://mlsanity.com/paper/2304.10946/) (2023)

## Built on by (most cited first, 27 of 27)

- [A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers](https://mlsanity.com/paper/2508.21148/) (2025)
- [NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences](https://mlsanity.com/paper/2503.07599/) (2025)
- [A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives](https://mlsanity.com/paper/2504.20069/) (2025)
- [LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis](https://mlsanity.com/paper/2604.28178/) (2026)
- [A New Era in Human Factors Engineering: A Survey of the Applications and Prospects of Large Multimodal Models](https://mlsanity.com/paper/2405.13426/) (2024)
- [Large Language Models for EEG: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2506.06353/) (2025)
- [Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleep](https://mlsanity.com/paper/2408.07822/) (2024)
- [Foundation Models for Cross-Domain EEG Analysis Application: A Survey](https://mlsanity.com/paper/2508.15716/) (2025)
- [NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines](https://mlsanity.com/paper/2602.13473/) (2026)
- [A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications](https://mlsanity.com/paper/2410.08224/) (2024)
- [Large Language Models for Bioinformatics](https://mlsanity.com/paper/2501.06271/) (2025)
- [AAD-LLM: Neural Attention-Driven Auditory Scene Understanding](https://mlsanity.com/paper/2502.16794/) (2025)
- [Large Language models for Time Series Analysis: Techniques, Applications, and Challenges](https://mlsanity.com/paper/2506.11040/) (2025)
- [From Theory to Application: Fine-Tuning Large EEG Model with Real-World Stress Data](https://mlsanity.com/paper/2505.23042/) (2025)
- [Generalist vs Specialist Time Series Foundation Models: Investigating Potential Emergent Behaviors in Assessing Human Health Using PPG Signals](https://mlsanity.com/paper/2510.14254/) (2025)
- [Genomic Next-Token Predictors are In-Context Learners](https://mlsanity.com/paper/2511.12797/) (2025)
- [Mechanistic Interpretability of Brain-to-Speech Models Across Speech Modes](https://mlsanity.com/paper/2602.01247/) (2026)
- [CogEEGAgent: Toward Autonomous Cognitive EEG Analysis with Grounded Execution and Selection-Aware Verification](https://mlsanity.com/paper/2607.25045/) (2026)
- [Convergent Emergence of In-Context Learning Across Modalities](https://mlsanity.com/paper/2609.14011/) (2026)
- [Leveraging Foundation Models for Calibration-Free c-VEP BCIs](https://mlsanity.com/paper/2601.06028/) (2025)
- [NeuroCanvas: VLLM-Powered Robust Seizure Detection by Reformulating Multichannel EEG as Image](https://mlsanity.com/paper/2602.04769/) (2026)
- [Bridging the Compression-Precision Paradox: A Hybrid Architecture for Clinical EEG Report Generation with Guaranteed Measurement Accuracy](https://mlsanity.com/paper/2602.10544/) (2026)
- [Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs](https://mlsanity.com/paper/2605.18172/) (2026)
- [EEG-SpikeAgent: Agentic Closed-Loop Program Synthesis for Automated EEG Spike Detection](https://mlsanity.com/paper/2607.04558/) (2026)
- [EEG-Xplain: Decoding Neural Black-Boxes of EEG Foundation Models](https://mlsanity.com/paper/2609.15687/) (2026)
- [Large language models in medical time series analysis](https://mlsanity.com/paper/2609.22262/) (2026)
- [Multimodal LLMs Can Learn to Read Brain Signals: A Vision--Language Model for Unified Multi-Task EEG Decoding](https://mlsanity.com/paper/2610.09355/) (2026)

## Abstract (arXiv)

In conventional machine learning (ML) approaches applied to electroencephalography (EEG), this is often a limited focus, isolating specific brain activities occurring across disparate temporal scales (from transient spikes in milliseconds to seizures lasting minutes) and spatial scales (from localized high-frequency oscillations to global sleep activity). This siloed approach limits the development EEG ML models that exhibit multi-scale electrophysiological understanding and classification capabilities. Moreover, typical ML EEG approaches utilize black-box approaches, limiting their interpretability and trustworthiness in clinical contexts. Thus, we propose EEG-GPT, a unifying approach to EEG classification that leverages advances in large language models (LLM). EEG-GPT achieves excellent performance comparable to current state-of-the-art deep learning methods in classifying normal from abnormal EEG in a few-shot learning paradigm utilizing only 2% of training data. Furthermore, it offers the distinct advantages of providing intermediate reasoning steps and coordinating specialist EEG tools across multiple scales in its operation, offering transparent and interpretable step-by-step verification, thereby promoting trustworthiness in clinical contexts.