# KnowEEG: Explainable Knowledge Driven EEG Classification

arXiv: https://arxiv.org/abs/2505.00541 · HTML: https://mlsanity.com/paper/2505.00541/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 62 references that are in the map.
- First posted on arXiv 1 May 2025.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [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)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [catch22: CAnonical Time-series CHaracteristics](https://mlsanity.com/paper/1901.10200/) (2019)
- [MAEEG: Masked Auto-encoder for EEG Representation Learning](https://mlsanity.com/paper/2211.02625/) (2022)
- [EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs](https://mlsanity.com/paper/2402.17772/) (2024)
- [MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis](https://mlsanity.com/paper/2002.09283/) (2020)
- [Improved EEG Event Classification Using Differential Energy](https://mlsanity.com/paper/1801.02477/) (2018)
- [Self-supervised Learning for Electroencephalogram: A Systematic Survey](https://mlsanity.com/paper/2401.05446/) (2024)
- [A Survey on State-of-the-art Deep Learning Applications and Challenges](https://mlsanity.com/paper/2403.17561/) (2024)
- [Interpretable Classification of Early Stage Parkinson's Disease from EEG](https://mlsanity.com/paper/2301.09568/) (2023)

## Abstract (arXiv)

Electroencephalography (EEG) classification has shown significant promise in applications ranging from neurodegenerative disease detection to brain-computer interfaces (BCIs). Whilst deep learning methods have increasingly dominated the research landscape in recent years; these models remain largely opaque 'black boxes' with limited interpretability. In this paper, we revisit the utility of feature-based classification via a systematic pipeline designed to integrate per-electrode signal statistics with cross-brain connectivity metrics. The proposed "KnowEEG" pipeline extracts 783 per-electrode statistical features alongside between-electrode connectivity statistics, utilizing a balanced sampling strategy between high-dimensional per electrode features and connectivity. This feature-based pipeline achieves performance comparable to or exceeding state-of-the-art deep learning models across five diverse classification tasks: emotion detection, mental workload classification, eyes open/closed detection, abnormal EEG classification, and event detection. This performance is achieved without the need for Graphics Processing Unit (GPU) resources. Furthermore, the pipeline provides inherent explainability through feature importance rankings. We demonstrate via the eyes open/closed task that this explainability identifies discriminative biomarkers in the occipital region that are consistent with established neuroscience literature. These results suggest that despite significant advances in deep learning, structured feature engineering remains a high performance and interpretable method for EEG classification.