# Deep learning-based electroencephalography analysis: a systematic review

arXiv: https://arxiv.org/abs/1901.05498 · HTML: https://mlsanity.com/paper/1901.05498/

## Key facts

- Cited by 156 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2019.

## Built on by (most cited first, 30 of 156)

- [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)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [Brain decoding: toward real-time reconstruction of visual perception](https://mlsanity.com/paper/2310.19812/) (2023)
- [FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface](https://mlsanity.com/paper/2104.01233/) (2021)
- [EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs](https://mlsanity.com/paper/2402.17772/) (2024)
- [MAtt: A Manifold Attention Network for EEG Decoding](https://mlsanity.com/paper/2210.01986/) (2022)
- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis](https://mlsanity.com/paper/2112.12744/) (2021)
- [Data augmentation for learning predictive models on EEG: a systematic comparison](https://mlsanity.com/paper/2206.14483/) (2022)
- [EEG Foundation Models: A Critical Review of Current Progress and Future Directions](https://mlsanity.com/paper/2507.11783/) (2025)
- [The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark](https://mlsanity.com/paper/2404.15319/) (2024)
- [CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals](https://mlsanity.com/paper/2106.13695/) (2021)
- [GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition](https://mlsanity.com/paper/2109.03124/) (2021)
- [FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model](https://mlsanity.com/paper/2502.06438/) (2025)
- [Robust learning from corrupted EEG with dynamic spatial filtering](https://mlsanity.com/paper/2105.12916/) (2021)
- [GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition](https://mlsanity.com/paper/2205.01030/) (2022)
- [CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention](https://mlsanity.com/paper/2501.10885/) (2025)
- [End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax](https://mlsanity.com/paper/2102.09050/) (2021)
- [Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture](https://mlsanity.com/paper/2105.13854/) (2021)
- [A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG](https://mlsanity.com/paper/2106.00613/) (2021)
- [Calibration-free online test-time adaptation for electroencephalography motor imagery decoding](https://mlsanity.com/paper/2311.18520/) (2023)
- [A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding](https://mlsanity.com/paper/2401.10746/) (2024)
- [Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2112.06654/) (2021)
- [EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification](https://mlsanity.com/paper/2204.06947/) (2022)
- [Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification](https://mlsanity.com/paper/2301.05869/) (2023)
- [BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesis](https://mlsanity.com/paper/2401.10282/) (2024)
- [EEG-CLIP : Learning EEG representations from natural language descriptions](https://mlsanity.com/paper/2503.16531/) (2025)
- [Evaluating the structure of cognitive tasks with transfer learning](https://mlsanity.com/paper/2308.02408/) (2023)
- [Time Majority Voting, a PC-based EEG Classifier for Non-expert Users](https://mlsanity.com/paper/2207.12662/) (2022)
- [Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers](https://mlsanity.com/paper/2307.02819/) (2023)

## Abstract (arXiv)

Electroencephalography (EEG) is a complex signal and can require several years of training to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches in order to inform future research and formulate recommendations. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours. As for the model, 40% of the studies used convolutional neural networks (CNNs), while 14% used recurrent neural networks (RNNs), most often with a total of 3 to 10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was 5.4% across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. To help the field progress, we provide a list of recommendations for future studies and we make our summary table of DL and EEG papers available and invite the community to contribute.