Are foundation models useful feature extractors for electroencephalography analysis?
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866914306970353664 |
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| author | Turgut, Özgün Bott, Felix S. Ploner, Markus Rueckert, Daniel |
| author_facet | Turgut, Özgün Bott, Felix S. Ploner, Markus Rueckert, Daniel |
| contents | The success of foundation models in natural language processing and computer vision has motivated similar approaches in time series analysis. While foundational time series models have proven beneficial on a variety of tasks, their effectiveness in medical applications with limited data remains underexplored. In this work, we investigate this question in the context of electroencephalography (EEG) by evaluating general-purpose time series models on age prediction, seizure detection, and classification of clinically relevant EEG events. We compare their diagnostic performance against specialised EEG models and assess the quality of the extracted features. The results show that general-purpose models are competitive and capture features useful to localising demographic and disease-related biomarkers. These findings indicate that foundational time series models can reduce the reliance on large task-specific datasets and models, making them valuable in clinical practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_21086 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Are foundation models useful feature extractors for electroencephalography analysis? Turgut, Özgün Bott, Felix S. Ploner, Markus Rueckert, Daniel Artificial Intelligence Machine Learning The success of foundation models in natural language processing and computer vision has motivated similar approaches in time series analysis. While foundational time series models have proven beneficial on a variety of tasks, their effectiveness in medical applications with limited data remains underexplored. In this work, we investigate this question in the context of electroencephalography (EEG) by evaluating general-purpose time series models on age prediction, seizure detection, and classification of clinically relevant EEG events. We compare their diagnostic performance against specialised EEG models and assess the quality of the extracted features. The results show that general-purpose models are competitive and capture features useful to localising demographic and disease-related biomarkers. These findings indicate that foundational time series models can reduce the reliance on large task-specific datasets and models, making them valuable in clinical practice. |
| title | Are foundation models useful feature extractors for electroencephalography analysis? |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.21086 |