Artificial neural networks applied to somatosensory evoked potentials for migraine classification
Our findings suggest that ANNs trained with SSEP-derived variables hold promise as a noninvasive tool for migraine classification, offering potential for clinical application and deeper insights into migraine diagnostics.
Authors
- Cherubino Di Lorenzo (ORCID: https://orcid.org/0000-0003-0845-5694)
- Gabriele Sebastianelli (ORCID: https://orcid.org/0000-0002-4231-4417)
- Gianluca Coppola (ORCID: https://orcid.org/0000-0002-8510-6925)
- Daniele Secci (ORCID: https://orcid.org/0000-0002-0605-0741)
- Mariano Serrao (ORCID: https://orcid.org/0000-0003-3031-680X)
- Fu‐Jung Hsiao (ORCID: https://orcid.org/0000-0001-7824-8864)
- Francesco Casillo (ORCID: https://orcid.org/0000-0003-4642-950X)
- Chiara Abagnale
Institutions
- University of Parma (IT)
- National Yang Ming Chiao Tung University (TW)
- Università Campus Bio-Medico (IT)
- Taipei Veterans General Hospital (TW)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- The Journal of Headache and Pain
- Published
- 2025-04-04
- DOI
- https://doi.org/10.1186/s10194-025-01989-2
- Citations
- 2
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 2.86