# Deep learning applied to EEG data with different montages using spatial attention

arXiv: https://arxiv.org/abs/2310.10550 · HTML: https://mlsanity.com/paper/2310.10550/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing](https://mlsanity.com/paper/1905.07039/) (2019)
- [Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral Features](https://mlsanity.com/paper/2105.04762/) (2021)
- [Assessing learned features of Deep Learning applied to EEG](https://mlsanity.com/paper/2111.04309/) (2021)
- [A streamable large-scale clinical EEG dataset for Deep Learning](https://mlsanity.com/paper/2203.02552/) (2022)

## Built on by (most cited first, 2 of 2)

- [Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets](https://mlsanity.com/paper/2403.15415/) (2024)
- [A Silent Speech Decoding System from EEG and EMG with Heterogenous Electrode Configurations](https://mlsanity.com/paper/2506.13835/) (2025)

## Abstract (arXiv)

The ability of Deep Learning to process and extract relevant information in complex brain dynamics from raw EEG data has been demonstrated in various recent works. Deep learning models, however, have also been shown to perform best on large corpora of data. When processing EEG, a natural approach is to combine EEG datasets from different experiments to train large deep-learning models. However, most EEG experiments use custom channel montages, requiring the data to be transformed into a common space. Previous methods have used the raw EEG signal to extract features of interest and focused on using a common feature space across EEG datasets. While this is a sensible approach, it underexploits the potential richness of EEG raw data. Here, we explore using spatial attention applied to EEG electrode coordinates to perform channel harmonization of raw EEG data, allowing us to train deep learning on EEG data using different montages. We test this model on a gender classification task. We first show that spatial attention increases model performance. Then, we show that a deep learning model trained on data using different channel montages performs significantly better than deep learning models trained on fixed 23- and 128-channel data montages.