ArticlePloS one2018
The blessing of Dimensionality: Feature Selection outperforms functional connectivity-based feature transformation to classify ADHD subjects from EEG patterns of phase synchronisation.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Individualized prediction models in ADHD: a systematic review and meta-regression.Molecular psychiatry · 2024Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- A comprehensive investigation into EEG spectral analysis for ADHD brain dominance.AIMS neuroscience · 2026Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials.Cognitive neurodynamics · 2022Article
- What Has Been Learned from Using EEG Methods in Research of ADHD?Current topics in behavioral neurosciences · 2022Article
- Modifications in the Topological Structure of EEG Functional Connectivity Networks during Listening Tonal and Atonal Concert Music in Musicians and Non-Musicians.Brain sciences · 2021Article
- Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms.Computational and mathematical methods in medicine · 2021Article
- Altered Functional Connectivity in Children with ADHD Revealed by Scalp EEG: An ERP Study.Neural plasticity · 2021Article
- High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality.Entropy (Basel, Switzerland) · 2020Review
- Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks.Frontiers in neuroscience · 2020Article
- Biomagnetic biomarkers for dementia: A pilot multicentre study with a recommended methodological framework for magnetoencephalography.Alzheimer's & dementia (Amsterdam, Netherlands) · 2019Article
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6 authors.
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Abstract
Functional connectivity (FC) characterizes brain activity from a multivariate set of N brain signals by means of an NxN matrix A, whose elements estimate the dependence within each possible pair of signals. Such matrix can be used as a feature vector for (un)supervised subject classification. Yet if N is large, A is highly dimensional. Little is known on the effect that different strategies to reduce its dimensionality may have on its classification ability. Here, we apply different machine learning algorithms to classify 33 children (age [6-14 years]) into two groups (healthy controls and Attention Deficit Hyperactivity Disorder patients) using EEG FC patterns obtained from two phase synchronisation indices. We found that the classification is highly successful (around 95%) if the whole matrix A is taken into account, and the relevant features are selected using machine learning methods. However, if FC algorithms are applied instead to transform A into a lower dimensionality matrix, the classification rate drops to less than 80%. We conclude that, for the purpose of pattern classification, the relevant features should be selected among the elements of A by using appropriate machine learning algorithms.
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