# Machine-Learning-Based Diagnostics of EEG Pathology

arXiv: https://arxiv.org/abs/2002.05115 · HTML: https://mlsanity.com/paper/2002.05115/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

## Built on by (most cited first, 30 of 35)

- [BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data](https://mlsanity.com/paper/2101.12037/) (2021)
- [SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG](https://mlsanity.com/paper/2206.01323/) (2022)
- [Data augmentation for learning predictive models on EEG: a systematic comparison](https://mlsanity.com/paper/2206.14483/) (2022)
- [CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals](https://mlsanity.com/paper/2106.13695/) (2021)
- [NeuralBench: A Unifying Framework to Benchmark NeuroAI Models](https://mlsanity.com/paper/2605.08495/) (2026)
- [Robust learning from corrupted EEG with dynamic spatial filtering](https://mlsanity.com/paper/2105.12916/) (2021)
- [EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding](https://mlsanity.com/paper/2506.19141/) (2025)
- [EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification](https://mlsanity.com/paper/2204.06947/) (2022)
- [Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography](https://mlsanity.com/paper/2207.01391/) (2022)
- [Geodesic Optimization for Predictive Shift Adaptation on EEG data](https://mlsanity.com/paper/2407.03878/) (2024)
- [On the interpretation of linear Riemannian tangent space model parameters in M/EEG](https://mlsanity.com/paper/2107.14398/) (2021)
- [Evaluating the structure of cognitive tasks with transfer learning](https://mlsanity.com/paper/2308.02408/) (2023)
- [EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping](https://mlsanity.com/paper/2409.07480/) (2024)
- [Is Limited Participant Diversity Impeding EEG-based Machine Learning?](https://mlsanity.com/paper/2503.13497/) (2025)
- [Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets](https://mlsanity.com/paper/2403.15415/) (2024)
- [An EEG-based approach for Parkinson's disease diagnosis using Capsule network](https://mlsanity.com/paper/2201.00628/) (2021)
- [MP-SeizNet: A Multi-Path CNN Bi-LSTM Network for Seizure-Type Classification Using EEG](https://mlsanity.com/paper/2211.04628/) (2022)
- [Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning](https://mlsanity.com/paper/2309.10910/) (2023)
- [A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection](https://mlsanity.com/paper/2111.08457/) (2021)
- [Brain Age Revisited: Investigating the State vs. Trait Hypotheses of EEG-derived Brain-Age Dynamics with Deep Learning](https://mlsanity.com/paper/2310.07029/) (2023)
- [A spectral audit framework reveals task-dependent aperiodic reliance across EEG and ECG deep learning](https://mlsanity.com/paper/2606.08583/) (2026)
- [Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification](https://mlsanity.com/paper/2106.10633/) (2021)
- [Convolutional Neural Networks with A Topographic Representation Module for EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2208.10708/) (2022)
- [Scope and Arbitration in Machine Learning Clinical EEG Classification](https://mlsanity.com/paper/2303.06386/) (2023)
- [Window Stacking Meta-Models for Clinical EEG Classification](https://mlsanity.com/paper/2401.10283/) (2024)
- [EEG classifier cross-task transfer to avoid training sessions in robot-assisted rehabilitation](https://mlsanity.com/paper/2402.17790/) (2024)
- [Embedded Deployment of Semantic Segmentation in Medicine through Low-Resolution Inputs](https://mlsanity.com/paper/2403.05340/) (2024)
- [Bayesian uncertainty-aware deep learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection](https://mlsanity.com/paper/2410.19815/) (2024)
- [Automatic detection of abnormal clinical EEG: comparison of a finetuned foundation model with two deep learning models](https://mlsanity.com/paper/2505.21507/) (2025)
- [Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction](https://mlsanity.com/paper/2508.08724/) (2025)

## Abstract (arXiv)

Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding have typically analyzed a limited number of features, decoders, or both. For a I) more elaborate feature-based EEG analysis, and II) in-depth comparisons of both approaches, here we first develop a comprehensive feature-based framework, and then compare this framework to state-of-the-art end-to-end methods. To this aim, we apply the proposed feature-based framework and deep neural networks including an EEG-optimized temporal convolutional network (TCN) to the task of pathological versus non-pathological EEG classification. For a robust comparison, we chose the Temple University Hospital (TUH) Abnormal EEG Corpus (v2.0.0), which contains approximately 3000 EEG recordings. The results demonstrate that the proposed feature-based decoding framework can achieve accuracies on the same level as state-of-the-art deep neural networks. We find accuracies across both approaches in an astonishingly narrow range from 81--86%. Moreover, visualizations and analyses indicated that both approaches used similar aspects of the data, e.g., delta and theta band power at temporal electrode locations. We argue that the accuracies of current binary EEG pathology decoders could saturate near 90% due to the imperfect inter-rater agreement of the clinical labels, and that such decoders are already clinically useful, such as in areas where clinical EEG experts are rare. We make the proposed feature-based framework available open source and thus offer a new tool for EEG machine learning research.