# Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

arXiv: https://arxiv.org/abs/2011.11128 · HTML: https://mlsanity.com/paper/2011.11128/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [MindBigData 2022 A Large Dataset of Brain Signals](https://mlsanity.com/paper/2212.14746/) (2022)
- [Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers](https://mlsanity.com/paper/2307.02819/) (2023)
- [Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding](https://mlsanity.com/paper/2409.03251/) (2024)
- [A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond](https://mlsanity.com/paper/2502.12048/) (2025)
- [Feature Reweighting for EEG-based Motor Imagery Classification](https://mlsanity.com/paper/2308.02515/) (2023)
- [Reducing Intraspecies and Interspecies Covariate Shift in Traumatic Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment](https://mlsanity.com/paper/2310.02398/) (2023)
- [D-FaST: Cognitive Signal Decoding with Disentangled Frequency-Spatial-Temporal Attention](https://mlsanity.com/paper/2406.02602/) (2024)
- [CATVis: Context-Aware Thought Visualization](https://mlsanity.com/paper/2507.11522/) (2025)
- [DLGE: Dual Local-Global Encoding for Generalizable Cross-BCI-Paradigm](https://mlsanity.com/paper/2509.07991/) (2025)
- [One Model for All: Universal Pre-training for EEG based Emotion Recognition across Heterogeneous Datasets and Paradigms](https://mlsanity.com/paper/2511.08444/) (2025)

## Abstract (arXiv)

Deep learning has achieved excellent performance in a wide range of domains, especially in speech recognition and computer vision. Relatively less work has been done for EEG, but there is still significant progress attained in the last decade. Due to the lack of a comprehensive and topic widely covered survey for deep learning in EEG, we attempt to summarize recent progress to provide an overview, as well as perspectives for future developments. We first briefly mention the artifacts removal for EEG signal and then introduce deep learning models that have been utilized in EEG processing and classification. Subsequently, the applications of deep learning in EEG are reviewed by categorizing them into groups such as brain-computer interface, disease detection, and emotion recognition. They are followed by the discussion, in which the pros and cons of deep learning are presented and future directions and challenges for deep learning in EEG are proposed. We hope that this paper could serve as a summary of past work for deep learning in EEG and the beginning of further developments and achievements of EEG studies based on deep learning.