# Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

arXiv: https://arxiv.org/abs/1708.08012 · HTML: https://mlsanity.com/paper/1708.08012/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2017.

## Built on by (most cited first, 13 of 13)

- [Robust learning from corrupted EEG with dynamic spatial filtering](https://mlsanity.com/paper/2105.12916/) (2021)
- [EEG-CLIP : Learning EEG representations from natural language descriptions](https://mlsanity.com/paper/2503.16531/) (2025)
- [WaveMind: Towards a Conversational EEG Foundation Model Aligned to Textual and Visual Modalities](https://mlsanity.com/paper/2510.00032/) (2025)
- [MP-SeizNet: A Multi-Path CNN Bi-LSTM Network for Seizure-Type Classification Using EEG](https://mlsanity.com/paper/2211.04628/) (2022)
- [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)
- [GEPD:GAN-Enhanced Generalizable Model for EEG-Based Detection of Parkinson's Disease](https://mlsanity.com/paper/2508.14074/) (2025)
- [Unveiling Deep Semantic Uncertainty Perception for Language-Anchored Multi-modal Vision-Brain Alignment](https://mlsanity.com/paper/2511.04078/) (2025)
- [Product semantics translation from brain activity via adversarial learning](https://mlsanity.com/paper/2103.15602/) (2021)
- [FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network](https://mlsanity.com/paper/2403.03526/) (2024)
- [AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on A Cue-Masked Paradigm](https://mlsanity.com/paper/2501.03571/) (2025)
- [Spiking Neural Network for Intra-cortical Brain Signal Decoding](https://mlsanity.com/paper/2504.09213/) (2025)
- [NeuroPhysNet: A FitzHugh-Nagumo-Based Physics-Informed Neural Network Framework for Electroencephalograph (EEG) Analysis and Motor Imagery Classification](https://mlsanity.com/paper/2506.13222/) (2025)
- [Lightweight Transformer for EEG Classification via Balanced Signed Graph Algorithm Unrolling](https://mlsanity.com/paper/2510.03027/) (2025)

## Abstract (arXiv)

We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet architectures recently shown to decode task-related information from EEG at least as well as established algorithms designed for this purpose. In decoding EEG pathology, both ConvNets reached substantially better accuracies (about 6% better, ~85% vs. ~79%) than the only published result for this dataset, and were still better when using only 1 minute of each recording for training and only six seconds of each recording for testing. We used automated methods to optimize architectural hyperparameters and found intriguingly different ConvNet architectures, e.g., with max pooling as the only nonlinearity. Visualizations of the ConvNet decoding behavior showed that they used spectral power changes in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside other features, consistent with expectations derived from spectral analysis of the EEG data and from the textual medical reports. Analysis of the textual medical reports also highlighted the potential for accuracy increases by integrating contextual information, such as the age of subjects. In summary, the ConvNets and visualization techniques used in this study constitute a next step towards clinically useful automated EEG diagnosis and establish a new baseline for future work on this topic.