# A review of feature extraction and performance evaluation in epileptic seizure detection using EEG

arXiv: https://arxiv.org/abs/1908.00492 · HTML: https://mlsanity.com/paper/1908.00492/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 29 of 29)

- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives](https://mlsanity.com/paper/2504.20069/) (2025)
- [Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies](https://mlsanity.com/paper/2109.04364/) (2021)
- [Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification](https://mlsanity.com/paper/2301.05869/) (2023)
- [NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines](https://mlsanity.com/paper/2602.13473/) (2026)
- [An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2105.14278/) (2021)
- [Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG](https://mlsanity.com/paper/2301.03470/) (2023)
- [Genetic algorithm for feature selection of EEG heterogeneous data](https://mlsanity.com/paper/2103.07117/) (2021)
- [EEG Opto-processor: epileptic seizure detection using diffractive photonic computing units](https://mlsanity.com/paper/2301.10167/) (2022)
- [Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2412.03224/) (2024)
- [Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification](https://mlsanity.com/paper/2412.15224/) (2024)
- [Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review](https://mlsanity.com/paper/2102.03336/) (2021)
- [Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis](https://mlsanity.com/paper/2107.08514/) (2021)
- [A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection](https://mlsanity.com/paper/2111.08457/) (2021)
- [A review on Epileptic Seizure Detection using Machine Learning](https://mlsanity.com/paper/2210.06292/) (2022)
- [SincVAE: A new semi-supervised approach to improve anomaly detection on EEG data using SincNet and variational autoencoder](https://mlsanity.com/paper/2406.17537/) (2024)
- [Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms](https://mlsanity.com/paper/2411.11302/) (2024)
- [RL-BioAug: Label-Efficient Reinforcement Learning for Self-Supervised EEG Representation Learning](https://mlsanity.com/paper/2601.13964/) (2026)
- [Scalable Machine Learning Architecture for Neonatal Seizure Detection on Ultra-Edge Devices](https://mlsanity.com/paper/2111.15569/) (2021)
- [Automated Huntington's Disease Prognosis via Biomedical Signals and Shallow Machine Learning](https://mlsanity.com/paper/2302.03605/) (2023)
- [Entropy-based machine learning model for diagnosis and monitoring of Parkinson's Disease in smart IoT environment](https://mlsanity.com/paper/2309.07134/) (2023)
- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)
- [Comparison analysis between standard polysomnographic data and in-ear-EEG signals: A preliminary study](https://mlsanity.com/paper/2401.10107/) (2024)
- [Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification](https://mlsanity.com/paper/2411.19502/) (2024)
- [EAD: An EEG Adapter for Automated Classification](https://mlsanity.com/paper/2505.23107/) (2025)
- [Affordable EEG, Actionable Insights: An Open Dataset and Evaluation Framework for Epilepsy Patient Stratification](https://mlsanity.com/paper/2511.01879/) (2025)
- [NeuroNarrator: A Generalist EEG-to-Text Foundation Model for Clinical Interpretation via Spectro-Spatial Grounding and Temporal State-Space Reasoning](https://mlsanity.com/paper/2603.16880/) (2026)
- [Towards Unified Multi-task EEG Analysis with Low-Rank Adaptation](https://mlsanity.com/paper/2604.25131/) (2026)
- [NeuroOnline: Bridging Pretraining and Online Adaptation for EEG Foundation Models](https://mlsanity.com/paper/2607.03925/) (2026)

## Abstract (arXiv)

Since the manual detection of electrographic seizures in continuous electroencephalogram (EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop automatic seizure detection are diverse and ongoing. Machine learning approaches are intensely being applied to this problem due to their ability to classify seizure conditions from a large amount of data, and provide pre-screened results for neurologists. Several features, data transformations, and classifiers have been explored to analyze and classify seizures via EEG signals. In the literature, some jointly-applied features used in the classification may have shared similar contributions, making them redundant in the learning process. Therefore, this paper aims to comprehensively summarize feature descriptions and their interpretations in characterizing epileptic seizures using EEG signals, as well as to review classification performance metrics. To provide meaningful information of feature selection, we conducted an experiment to examine the quality of each feature independently. The Bayesian error and non-parametric probability distribution estimation were employed to determine the significance of the individual features. Moreover, a redundancy analysis using a correlation-based feature selection was applied. The results showed that the following features --variance, energy, nonlinear energy, and Shannon entropy computed on a raw EEG signal, as well as variance, energy, kurtosis, and line length calculated on wavelet coefficients-- were able to significantly capture the seizures. An improvement of 4.77--13.51% in the Bayesian error from the baseline was obtained.