Roberto Hornero

Full Professor

Universidad de Valladolid

Spain

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About

Roberto Hornero is a Full Professor at Universidad de Valladolid, Spain. His research areas encompass EEG biomarkers of cognitive load, ocular artifact reduction in EEG signals, and the effects of genetic variations on brain electrical activity. He also focuses on enhancing user experience in brain-computer interfaces and pioneering deep learning applications in medical imaging and diagnostics.

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Víctor Martínez-Cagigal

Assistant Professor

Universidad de Valladolid

SPAIN

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EDUARDO TAMAYO

Universidad de Valladolid

SPAIN

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Paulo Gonçalves

Professor Coordenador (Associate Professor)

Instituto Politécnico de Leiria

PORTUGAL

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Gonzalo C. Gutiérrez-Tobal

Universidad de Valladolid

SPAIN

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Carmen Requena-Hernández

SPAIN

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Pablo Núñez

Universidad de Valladolid

SPAIN

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Jesús Lázaro

Assistant Professor (Profesor Ayudante Doctor)

Universidad de Zaragoza

SPAIN

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Ermelinda Maria Bernardo Gonçalves Marques

Professora Adjunta

Instituto Politécnico da Guarda Escola Superior de Saúde

PORTUGAL

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Roberto Hornero

Research Interests

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Articles (20)

EEG biomarkers of cognitive load: Insights from incremental element encoding in short-term working memory

EuroAGE+: um projeto para melhorar a qualidade de vida dos idosos, através da utilização de tecnologias inovadoras e da cooperação transfronteiriça.

Effect of MAPT gene variations on the brain electrical activity: A multiplex network study

Deep Learning-Based Glioma Segmentation of 2D Intraoperative Ultrasound Images: A Multicenter Study Using the Brain Tumor Intraoperative Ultrasound Database (BraTioUS)

Calibration-free Ocular artifact reduction in EEG signals using a montage-independent deep learning model

Enhancing user experience in c-VEP-based BCI: Effects of visual stimulus opacity on performance and visual fatigue

Bridging motor execution and motor imagery BCI paradigms: An inter-task transfer learning approach

Beyond the Ground Truth, XGBoost Model Applied to Sleep Spindle Event Detection

Toward the integration of mixed reality and brain-computer interfaces based on code-modulated visual evoked potentials

An explainable deep-learning model reveals clinical clues in diabetic retinopathy through SHAP

Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses

Quantification of the robustness of functional neural networks: application to the characterization of Alzheimer’s disease continuum

Non-binary m-sequences for more comfortable brain–computer interfaces based on c-VEPs

ITACA: An open-source framework for Neurofeedback based on Brain–Computer Interfaces

MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research

Electrical analysis of logical complexity: an exploratory eeg study of logically valid/invalid deducive inference

An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea

Pediatric sleep apnea: Characterization of apneic events and sleep stages using heart rate variability

Gene Co-Expression Networks Offer New Perspectives on Sepsis Pathophysiology

A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry

Collaborators (14)

David Gozal

Fernando Vaquerizo-Villar

Jonathan Gomez-Raja

Carlos Gómez

Jesus Poza

Rosario Sarabia

Víctor Martínez-Cagigal

EDUARDO TAMAYO

Paulo Gonçalves

Gonzalo C. Gutiérrez-Tobal

Carmen Requena-Hernández

Pablo Núñez

Jesús Lázaro

Ermelinda Maria Bernardo Gonçalves Marques

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