ArticleSensors (Basel, Switzerland)2021
The Effects of Individual Differences, Non-Stationarity, and the Importance of Data Partitioning Decisions for Training and Testing of EEG Cross-Participant Models.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Transformer-Based Modeling of Directed Transfer Entropy Connectivity for EEG-Based ADHD Classification in Children.Sensors (Basel, Switzerland) · 2026Article
- Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2026Review
- Deception Detection from Five-Channel Wearable EEG on LieWaves: A Reproducible Baseline for Subject-Dependent and Subject-Independent Evaluation.Sensors (Basel, Switzerland) · 2026Article
- A 40-Class SSVEP Speller Dataset: Beta Range Stimulation for Low-Fatigue BCI Applications.Scientific data · 2025Article
- Detecting mind wandering via EEG and facial video features.Behavior research methods · 2025Article
- Home Robot Interaction Based on EEG Motor Imagery and Visual Perception Fusion.Sensors (Basel, Switzerland) · 2025Article
- Domain-generalized Deep Learning for Improved Subject-independent Emotion Recognition Based on Electroencephalography.Experimental neurobiology · 2025Article
- Exploring Task-Related EEG for Cross-Subject Early Alzheimer's Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis.Sensors (Basel, Switzerland) · 2024Article
- EEG-based emotional valence and emotion regulation classification: a data-centric and explainable approach.Scientific reports · 2024Article
- Power spectral density-based resting-state EEG classification of first-episode psychosis.Scientific reports · 2024Article
- EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning.Frontiers in human neuroscience · 2023Article
- EEG Signal Multichannel Frequency-Domain Ratio Indices for Drowsiness Detection Based on Multicriteria Optimization.Sensors (Basel, Switzerland) · 2021Article
- Generalized Deep Learning EEG Models for Cross-Participant and Cross-Task Detection of the Vigilance Decrement in Sustained Attention Tasks.Sensors (Basel, Switzerland) · 2021Article
- Intelligent Biosignal Analysis Methods.Sensors (Basel, Switzerland) · 2021Article
- A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
EEG-based deep learning models have trended toward models that are designed to perform classification on any individual (cross-participant models). However, because EEG varies across participants due to non-stationarity and individual differences, certain guidelines must be followed for partitioning data into training, validation, and testing sets, in order for cross-participant models to avoid overestimation of model accuracy. Despite this necessity, the majority of EEG-based cross-participant models have not adopted such guidelines. Furthermore, some data repositories may unwittingly contribute to the problem by providing partitioned test and non-test datasets for reasons such as competition support. In this study, we demonstrate how improper dataset partitioning and the resulting improper training, validation, and testing of a cross-participant model leads to overestimated model accuracy. We demonstrate this mathematically, and empirically, using five publicly available datasets. To build the cross-participant models for these datasets, we replicate published results and demonstrate how the model accuracies are significantly reduced when proper EEG cross-participant model guidelines are followed. Our empirical results show that by not following these guidelines, error rates of cross-participant models can be underestimated between 35% and 3900%. This misrepresentation of model performance for the general population potentially slows scientific progress toward truly high-performing classification models.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.