# Comparison of Attention-based Deep Learning Models for EEG Classification

arXiv: https://arxiv.org/abs/2012.01074 · HTML: https://mlsanity.com/paper/2012.01074/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis](https://mlsanity.com/paper/2104.08336/) (2021)
- [EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification](https://mlsanity.com/paper/2107.03983/) (2021)
- [High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm](https://mlsanity.com/paper/2107.10746/) (2021)
- [ACTA: A Mobile-Health Solution for Integrated Nudge-Neurofeedback Training for Senior Citizens](https://mlsanity.com/paper/2102.08692/) (2021)
- [Copilot-Assisted Second-Thought Framework for Brain-to-Robot Hand Motion Decoding](https://mlsanity.com/paper/2603.27492/) (2026)

## Abstract (arXiv)

Objective: To evaluate the impact on Electroencephalography (EEG) classification of different kinds of attention mechanisms in Deep Learning (DL) models. Methods: We compared three attention-enhanced DL models, the brand-new InstaGATs, an LSTM with attention and a CNN with attention. We used these models to classify normal and abnormal (i.e., artifactual or pathological) EEG patterns. Results: We achieved the state of the art in all classification problems, regardless the large variability of the datasets and the simple architecture of the attention-enhanced models. We could also prove that, depending on how the attention mechanism is applied and where the attention layer is located in the model, we can alternatively leverage the information contained in the time, frequency or space domain of the dataset. Conclusions: with this work, we shed light over the role of different attention mechanisms in the classification of normal and abnormal EEG patterns. Moreover, we discussed how they can exploit the intrinsic relationships in the temporal, frequency and spatial domains of our brain activity. Significance: Attention represents a promising strategy to evaluate the quality of the EEG information, and its relevance, in different real-world scenarios. Moreover, it can make it easier to parallelize the computation and, thus, to speed up the analysis of big electrophysiological (e.g., EEG) datasets.