104 citations · 215 across the 10 of their papers we have counts for
10 papers
Is K-fold cross validation the best model selection method for Machine Learning?
Juan M Gorriz, R. Martin Clemente, F Segovia +3
As a technique that can compactly represent complex patterns, machine learning has significant potential for predictive inference. K-fold cross-validation (CV) is the most common a…
EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia
Francisco Jesus Martinez-Murcia, Andrés Ortiz, Juan Manuel Górriz +4
The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal…
Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed?
Francisco J. Martinez-Murcia, Juan M. Górriz, Javier Ramírez +1
Spatial and intensity normalization are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are ke…
A hypothesis-driven method based on machine learning for neuroimaging data analysis
JM Gorriz, R. Martin-Clemente, C. G. Puntonet +3
There remains an open question about the usefulness and the interpretation of Machine learning (MLE) approaches for discrimination of spatial patterns of brain images between sampl…
Temporal EigenPAC for dyslexia diagnosis
Nicolás Gallego-Molina, Marco Formoso, Andrés Ortiz +2
Electroencephalography signals allow to explore the functional activity of the brain cortex in a non-invasive way. However, the analysis of these signals is not straightforward due…
Modelling Brain Connectivity Networks by Graph Embedding for Dyslexia Diagnosis
Marco A. Formoso, Andrés Ortiz, Francisco J. Martínez-Murcia +2
Several methods have been developed to extract information from electroencephalograms (EEG). One of them is Phase-Amplitude Coupling (PAC) which is a type of Cross-Frequency Coupli…