# EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters

arXiv: https://arxiv.org/abs/2110.10009 · HTML: https://mlsanity.com/paper/2110.10009/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 64 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [A Riemannian Network for SPD Matrix Learning](https://mlsanity.com/paper/1608.04233/) (2016)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Identifying Stable Patterns over Time for Emotion Recognition from EEG](https://mlsanity.com/paper/1601.02197/) (2016)
- [The Many-to-Many Mapping Between the Concordance Correlation Coefficient and the Mean Square Error](https://mlsanity.com/paper/1902.05180/) (2019)
- [Phase Locking Value revisited: teaching new tricks to an old dog](https://mlsanity.com/paper/1710.08037/) (2017)
- [Sinc-based convolutional neural networks for EEG-BCI-based motor imagery classification](https://mlsanity.com/paper/2101.10846/) (2021)
- [Towards the bio-personalization of music recommendation systems: A single-sensor EEG biomarker of subjective music preference](https://mlsanity.com/paper/1609.07365/) (2016)

## Built on by (most cited first, 4 of 4)

- [Latent Alignment with Deep Set EEG Decoders](https://mlsanity.com/paper/2311.17968/) (2023)
- [Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations](https://mlsanity.com/paper/2405.19345/) (2024)
- [Learning aligned EEG representations with subject-specific encoders](https://mlsanity.com/paper/2606.16462/) (2026)
- [EEG-D3: A Solution to the Hidden Overfitting Problem of Deep Learning Models](https://mlsanity.com/paper/2512.13806/) (2025)

## Abstract (arXiv)

Patterns of brain activity are associated with different brain processes and can be used to identify different brain states and make behavioral predictions. However, the relevant features are not readily apparent and accessible. To mine informative latent representations from multichannel recordings of ongoing EEG activity, we propose a novel differentiable decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. Specifically, we introduce filters parameterized by generalized Gaussian functions that offer a smooth derivative for stable end-to-end model training and allow for learning interpretable features. For the feature module, we use signal magnitude and functional connectivity estimates. We demonstrate the utility of our model towards emotion recognition from EEG signals on the SEED dataset, as well as on a new EEG dataset of unprecedented size (i.e., 761 subjects), where we identify consistent trends of music perception and related individual differences. The discovered features align with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening. This agrees with the respective specialisation of the temporal lobes regarding music perception proposed in the literature.