Unsupervised machine learning can delineate central sulcus by using the spatiotemporal characteristic of somatosensory evoked potentials.

Asman, Priscella; Prabhu, Sujit; Bastos, Dhiego; Tummala, Sudhakar; Bhavsar, Shreyas; McHugh, Thomas Michael; Ince, Nuri Firat

Asman, Priscella; Prabhu, Sujit; Bastos, Dhiego; Tummala, Sudhakar; Bhavsar, Shreyas; McHugh, Thomas Michael; Ince, Nuri Firat.

Afiliación

Asman P; Department of Biomedical Engineering, University of Houston, Houston, TX, United States of America.
Prabhu S; Department of Neurosurgery, UT MD Anderson Cancer Center, Houston, TX, United States of America.
Bastos D; Department of Neurosurgery, UT MD Anderson Cancer Center, Houston, TX, United States of America.
Tummala S; Department of Neurosurgery, UT MD Anderson Cancer Center, Houston, TX, United States of America.
Bhavsar S; Department of Anesthesiology, UT MD Anderson Cancer Center, Houston, TX, United States of America.
McHugh TM; Department of Anesthesiology, UT MD Anderson Cancer Center, Houston, TX, United States of America.
Ince NF; Department of Biomedical Engineering, University of Houston, Houston, TX, United States of America.

J Neural Eng ; 18(4)2021 04 29.

Article en En | MEDLINE | ID: mdl-33836520

Asunto(s)

Monitorización Neurofisiológica Intraoperatoria; Corteza Motora; Potenciales Evocados Somatosensoriales; Mano; Humanos; Aprendizaje Automático no Supervisado

Palabras clave

functional mapping; machine learning; somatosensory evoked potentials

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitorización Neurofisiológica Intraoperatoria / Corteza Motora Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Neural Eng Asunto de la revista: NEUROLOGIA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

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