# ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification

arXiv: https://arxiv.org/abs/1802.00308 · HTML: https://mlsanity.com/paper/1802.00308/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2018.

## Built on by (most cited first, 27 of 27)

- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [Domino: Discovering Systematic Errors with Cross-Modal Embeddings](https://mlsanity.com/paper/2203.14960/) (2022)
- [CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2506.23075/) (2025)
- [EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation](https://mlsanity.com/paper/2401.18006/) (2024)
- [A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives](https://mlsanity.com/paper/2504.20069/) (2025)
- [Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting](https://mlsanity.com/paper/2201.08780/) (2022)
- [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)
- [From Video to EEG: Adapting Joint Embedding Predictive Architecture to Uncover Saptiotemporal Dynamics in Brain Signal Analysis](https://mlsanity.com/paper/2507.03633/) (2025)
- [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)
- [Partial Label Learning for Emotion Recognition from EEG](https://mlsanity.com/paper/2302.13170/) (2023)
- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [ALFEE: Adaptive Large Foundation Model for EEG Representation](https://mlsanity.com/paper/2505.06291/) (2025)
- [MP-SeizNet: A Multi-Path CNN Bi-LSTM Network for Seizure-Type Classification Using EEG](https://mlsanity.com/paper/2211.04628/) (2022)
- [Bayesian Time-Series Classifier for Decoding Simple Visual Stimuli from Intracranial Neural Activity](https://mlsanity.com/paper/2307.15672/) (2023)
- [Neural Brain Fields: A NeRF-Inspired Approach for Generating Nonexistent EEG Electrodes](https://mlsanity.com/paper/2601.00012/) (2025)
- [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)
- [Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism](https://mlsanity.com/paper/2501.10342/) (2025)
- [Scope and Arbitration in Machine Learning Clinical EEG Classification](https://mlsanity.com/paper/2303.06386/) (2023)
- [Improving the Security of Smartwatch Payment with Deep Learning](https://mlsanity.com/paper/2307.05437/) (2023)
- [Window Stacking Meta-Models for Clinical EEG Classification](https://mlsanity.com/paper/2401.10283/) (2024)
- [UserBoost: Generating User-specific Synthetic Data for Faster Enrolment into Behavioural Biometric Systems](https://mlsanity.com/paper/2407.09104/) (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)
- [CRIA: A Cross-View Interaction and Instance-Adapted Pre-training Framework for Generalizable EEG Representations](https://mlsanity.com/paper/2506.16056/) (2025)
- [A Sliced-Wasserstein Framework on Correlation Matrices for EEG Decoding](https://mlsanity.com/paper/2606.06104/) (2026)
- [Reducing the Complexity of Deep Learning Models for EEG Analysis on Wearable Devices](https://mlsanity.com/paper/2606.12742/) (2026)
- [NeuroOnline: Bridging Pretraining and Online Adaptation for EEG Foundation Models](https://mlsanity.com/paper/2607.03925/) (2026)

## Abstract (arXiv)

Brain-related disorders such as epilepsy can be diagnosed by analyzing electroencephalograms (EEG). However, manual analysis of EEG data requires highly trained clinicians, and is a procedure that is known to have relatively low inter-rater agreement (IRA). Moreover, the volume of the data and the rate at which new data becomes available make manual interpretation a time-consuming, resource-hungry, and expensive process. In contrast, automated analysis of EEG data offers the potential to improve the quality of patient care by shortening the time to diagnosis and reducing manual error. In this paper, we focus on one of the first steps in interpreting an EEG session - identifying whether the brain activity is abnormal or normal. To solve this task, we propose a novel recurrent neural network (RNN) architecture termed ChronoNet which is inspired by recent developments from the field of image classification and designed to work efficiently with EEG data. ChronoNet is formed by stacking multiple 1D convolution layers followed by deep gated recurrent unit (GRU) layers where each 1D convolution layer uses multiple filters of exponentially varying lengths and the stacked GRU layers are densely connected in a feed-forward manner. We used the recently released TUH Abnormal EEG Corpus dataset for evaluating the performance of ChronoNet. Unlike previous studies using this dataset, ChronoNet directly takes time-series EEG as input and learns meaningful representations of brain activity patterns. ChronoNet outperforms the previously reported best results by 7.79% thereby setting a new benchmark for this dataset. Furthermore, we demonstrate the domain-independent nature of ChronoNet by successfully applying it to classify speech commands.