Cargando…
Double-Step Machine Learning Based Procedure for HFOs Detection and Classification
The need for automatic detection and classification of high-frequency oscillations (HFOs) as biomarkers of the epileptogenic tissue is strongly felt in the clinical field. In this context, the employment of artificial intelligence methods could be the missing piece to achieve this goal. This work pr...
Autores principales: | Sciaraffa, Nicolina, Klados, Manousos A., Borghini, Gianluca, Di Flumeri, Gianluca, Babiloni, Fabio, Aricò, Pietro |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7226084/ https://www.ncbi.nlm.nih.gov/pubmed/32276318 http://dx.doi.org/10.3390/brainsci10040220 |
Ejemplares similares
-
Correlation and Similarity between Cerebral and Non-Cerebral Electrical Activity for User’s States Assessment
por: Borghini, Gianluca, et al.
Publicado: (2019) -
The Dry Revolution: Evaluation of Three Different EEG Dry Electrode Types in Terms of Signal Spectral Features, Mental States Classification and Usability
por: Di Flumeri, Gianluca, et al.
Publicado: (2019) -
Neurophysiological Vigilance Characterisation and Assessment: Laboratory and Realistic Validations Involving Professional Air Traffic Controllers
por: Sebastiani, Marika, et al.
Publicado: (2020) -
Joint Analysis of Eye Blinks and Brain Activity to Investigate Attentional Demand during a Visual Search Task
por: Sciaraffa, Nicolina, et al.
Publicado: (2021) -
A New Perspective for the Training Assessment: Machine Learning-Based Neurometric for Augmented User's Evaluation
por: Borghini, Gianluca, et al.
Publicado: (2017)