ArticleFrontiers in neuroscience2024
Data leakage in deep learning studies of translational EEG.
Article in Frontiers in neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Electroencephalography in Subjective Cognitive Decline and Mild Cognitive Impairment: Systematic Review of Biomarkers, Classification, and Prognostic Evidence.Journal of medical Internet research · 2026Pooled it
- Machine learning performance in EEG-based mental workload classification across task types: a systematic review.Frontiers in neuroergonomics · 2025Pooled it
- Evaluating Validation Strategies in Motor Imagery EEG: A Full-Cohort GAF-PLV Analysis and Matched Sensitivity Study.Brain sciences · 2026Article
- Disentangling EEG Fingerprinting and Sleep Biomarkers Using Generalized Weighted Ordinal Patterns.Brain sciences · 2026Article
- Benchmarking Multimodal Workload Classification: Effects of Modality, Validation Protocol, and Segmentation Contrast on an Open Graded-Arithmetic Dataset.Bioengineering (Basel, Switzerland) · 2026Article
- Validation-Aware Retrospective EEG Treatment-Response Modelling Using Chaotic Pattern of Prime Numbers Features: Segment-Level Separability and Subject-Wise Generalisation.Bioengineering (Basel, Switzerland) · 2026Article
- Article
- Comprehensive benchmarking and explainable machine learning analysis of EEG imagery activity recognition.Scientific reports · 2026Article
- [Research on weighted hypergraph attention neural network for the diagnosis of psychiatric disorders using brain functional connectivity networks].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Configurable Modular EEG Classification Framework with Multiscale Features and Ensemble Learning: A Reproducible Evaluation for Schizophrenia Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Depression Detection from Three-Channel Resting-State EEG Using a Hybrid Conv1D and Spectral-Statistical Fusion Model.Sensors (Basel, Switzerland) · 2026Article
- Unsupervised Representation Learning Generates Differentiable Neurophysiological Profiles.bioRxiv : the preprint server for biology · 2026Article
- Dynamic Mode Decomposition-Based Clustered Pattern Projection for Reliable Alzheimer's Disease Detection from EEG.Diagnostics (Basel, Switzerland) · 2026Article
- Towards Multi-Brain Decoding in Autism: A Self-Supervised Learning Approach.Neuroinformatics · 2026Article
- Generalization or mirage? Data leakage and reported performance in neonatal EEG seizure detection models: a systematic review.BioData mining · 2026Review
- Clustered pattern projection for EEG dementia classification: evaluating the reliability of disorder patterns.Frontiers in neuroscience · 2026Article
- Explainable machine learning for Alzheimer's disease characterization using small-sample EEG data.Frontiers in aging neuroscience · 2026Article
- Interpretable machine-learning for depression classification based on a three-component EEG marker.Frontiers in computational neuroscience · 2026Article
- Window leakage in clinical EEG machine learning: matched subject-independent evaluation across ADHD and Parkinson cohorts.Frontiers in neuroinformatics · 2026Article
- Conditional spatial classification of expert-confirmed interictal epileptiform discharge epochs: An EEG-ECG ablation and SHAP analysis.PloS one · 2026Article
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10 authors.
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Abstract
A growing number of studies apply deep neural networks (DNNs) to recordings of human electroencephalography (EEG) to identify a range of disorders. In many studies, EEG recordings are split into segments, and each segment is randomly assigned to the training or test set. As a consequence, data from individual subjects appears in both the training and the test set. Could high test-set accuracy reflect data leakage from subject-specific patterns in the data, rather than patterns that identify a disease? We address this question by testing the performance of DNN classifiers using segment-based holdout (in which segments from one subject can appear in both the training and test set), and comparing this to their performance using subject-based holdout (where all segments from one subject appear exclusively in either the training set or the test set). In two datasets (one classifying Alzheimer's disease, and the other classifying epileptic seizures), we find that performance on previously-unseen subjects is strongly overestimated when models are trained using segment-based holdout. Finally, we survey the literature and find that the majority of translational DNN-EEG studies use segment-based holdout. Most published DNN-EEG studies may dramatically overestimate their classification performance on new subjects.
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