# Energy-Efficient Tree-Based EEG Artifact Detection

arXiv: https://arxiv.org/abs/2204.09577 · HTML: https://mlsanity.com/paper/2204.09577/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 19 Apr 2022.

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

- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Learning from Heterogeneous EEG Signals with Differentiable Channel Reordering](https://mlsanity.com/paper/2010.13694/) (2020)
- [Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices](https://mlsanity.com/paper/2106.08008/) (2021)
- [Wireless User-Generic Ear EEG](https://mlsanity.com/paper/2003.00657/) (2020)
- [Machine Learning for removing EEG artifacts: Setting the benchmark](https://mlsanity.com/paper/1903.07825/) (2019)
- [Optimizing Channel Selection for Seizure Detection](https://mlsanity.com/paper/1801.02472/) (2018)
- [High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm](https://mlsanity.com/paper/2107.10746/) (2021)

## Built on by (most cited first, 6 of 6)

- [LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis](https://mlsanity.com/paper/2510.22257/) (2025)
- [FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model](https://mlsanity.com/paper/2502.06438/) (2025)
- [BISeizuRe: BERT-Inspired Seizure Data Representation to Improve Epilepsy Monitoring](https://mlsanity.com/paper/2406.19189/) (2024)
- [EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems](https://mlsanity.com/paper/2309.07135/) (2023)
- [Skilog: A Smart Sensor System for Performance Analysis and Biofeedback in Ski Jumping](https://mlsanity.com/paper/2309.14455/) (2023)
- [ArtifactGen: Benchmarking WGAN-GP vs Diffusion for Label-Aware EEG Artifact Synthesis](https://mlsanity.com/paper/2509.08188/) (2025)

## Abstract (arXiv)

In the context of epilepsy monitoring, EEG artifacts are often mistaken for seizures due to their morphological similarity in both amplitude and frequency, making seizure detection systems susceptible to higher false alarm rates. In this work we present the implementation of an artifact detection algorithm based on a minimal number of EEG channels on a parallel ultra-low-power (PULP) embedded platform. The analyses are based on the TUH EEG Artifact Corpus dataset and focus on the temporal electrodes. First, we extract optimal feature models in the frequency domain using an automated machine learning framework, achieving a 93.95% accuracy, with a 0.838 F1 score for a 4 temporal EEG channel setup. The achieved accuracy levels surpass state-of-the-art by nearly 20%. Then, these algorithms are parallelized and optimized for a PULP platform, achieving a 5.21 times improvement of energy-efficient compared to state-of-the-art low-power implementations of artifact detection frameworks. Combining this model with a low-power seizure detection algorithm would allow for 300h of continuous monitoring on a 300 mAh battery in a wearable form factor and power budget. These results pave the way for implementing affordable, wearable, long-term epilepsy monitoring solutions with low false-positive rates and high sensitivity, meeting both patients' and caregivers' requirements.