# Bridging neuroscience and AI: a survey on large language models for neurological signal interpretation

## TL;DR

This survey explores how large language models can improve EEG signal analysis for neurological disease diagnosis with less data and training.

## Contribution

The paper introduces the novel application of LLMs to EEG analysis, emphasizing reduced training needs and increased transparency.

## Key findings

- LLMs can achieve expert-level performance in EEG diagnostics with minimal training data and fine-tuning.
- LLMs offer more reliable and transparent results by generating intermediate reasoning during analysis.
- Challenges include deployment issues, ethical concerns, and optimization bottlenecks with methods like Low-Rank Adaptation.

## Abstract

Electroencephalogram (EEG) signal analysis is important for the diagnosis of various neurological conditions. Traditional deep neural networks, such as convolutional networks, sequence-to-sequence networks, and hybrids of such neural networks were proven to be effective for a wide range of neurological disease classifications. However, these are limited by the requirement of a large dataset, extensive training, and hyperparameter tuning, which require expert-level machine learning knowledge. This survey paper aims to explore the ability of Large Language Models (LLMs) to transform existing systems of EEG-based disease diagnostics. LLMs have a vast background knowledge in neuroscience, disease diagnostics, and EEG signal processing techniques. Thus, these models are capable of achieving expert-level performance with minimal training data, nominal fine-tuning, and less computational overhead, leading to a shorter time to find effective solutions for diagnostics. Further, in comparison with traditional methods, LLM's capability to generate intermediate results and meaningful reasoning makes it more reliable and transparent. This paper delves into several use cases of LLM in EEG signal analysis and attempts to provide a comprehensive understanding of techniques in the domain that can be applied to different disease diagnostics. The study also strives to highlight challenges in the deployment of LLM models, ethical considerations, and bottlenecks in optimizing models due to requirements of specialized methods such as Low-Rank Adapation. In general, this survey aims to stimulate research in the area of EEG disease diagnostics by effectively using LLMs and associated techniques in machine learning pipelines.