Bridging neuroscience and AI: a survey on large language models for neurological signal interpretation
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 neurologi...
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| Format: | Article |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2025.1561401/full |
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| author | Sreejith Chandrasekharan Jisu Elsa Jacob |
| author_facet | Sreejith Chandrasekharan Jisu Elsa Jacob |
| author_sort | Sreejith Chandrasekharan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3b51b53816e64cd09fed2ffc62bfa543 |
| institution | OA Journals |
| issn | 1662-5196 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroinformatics |
| spelling | doaj-art-3b51b53816e64cd09fed2ffc62bfa5432025-08-20T02:35:51ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962025-06-011910.3389/fninf.2025.15614011561401Bridging neuroscience and AI: a survey on large language models for neurological signal interpretationSreejith Chandrasekharan0Jisu Elsa Jacob1Freelance Researcher, Trivandrum, Kerala, IndiaDepartment of Electronics and Communication Engineering, Sree Chitra Thirunal College of Engineering, Trivandrum, Kerala, IndiaElectroencephalogram (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.https://www.frontiersin.org/articles/10.3389/fninf.2025.1561401/fullelectroencephalogramlarge language modelLLMBERTGPT |
| spellingShingle | Sreejith Chandrasekharan Jisu Elsa Jacob Bridging neuroscience and AI: a survey on large language models for neurological signal interpretation Frontiers in Neuroinformatics electroencephalogram large language model LLM BERT GPT |
| title | Bridging neuroscience and AI: a survey on large language models for neurological signal interpretation |
| title_full | Bridging neuroscience and AI: a survey on large language models for neurological signal interpretation |
| title_fullStr | Bridging neuroscience and AI: a survey on large language models for neurological signal interpretation |
| title_full_unstemmed | Bridging neuroscience and AI: a survey on large language models for neurological signal interpretation |
| title_short | Bridging neuroscience and AI: a survey on large language models for neurological signal interpretation |
| title_sort | bridging neuroscience and ai a survey on large language models for neurological signal interpretation |
| topic | electroencephalogram large language model LLM BERT GPT |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2025.1561401/full |
| work_keys_str_mv | AT sreejithchandrasekharan bridgingneuroscienceandaiasurveyonlargelanguagemodelsforneurologicalsignalinterpretation AT jisuelsajacob bridgingneuroscienceandaiasurveyonlargelanguagemodelsforneurologicalsignalinterpretation |