# High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm

arXiv: https://arxiv.org/abs/2107.10746 · HTML: https://mlsanity.com/paper/2107.10746/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors](https://mlsanity.com/paper/2005.07186/) (2020)
- [Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference](https://mlsanity.com/paper/1506.02158/) (2015)
- [U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging](https://mlsanity.com/paper/1910.11162/) (2019)
- [Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation](https://mlsanity.com/paper/1805.02798/) (2018)
- [Subject-Aware Contrastive Learning for Biosignals](https://mlsanity.com/paper/2007.04871/) (2020)
- [Why should we add early exits to neural networks?](https://mlsanity.com/paper/2004.12814/) (2020)
- [Automated Design of Deep Learning Methods for Biomedical Image Segmentation](https://mlsanity.com/paper/1904.08128/) (2019)
- [Mixing-Specific Data Augmentation Techniques for Improved Blind Violin/Piano Source Separation](https://mlsanity.com/paper/2008.02480/) (2020)
- [Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data](https://mlsanity.com/paper/2104.02005/) (2021)
- [Comparison of Attention-based Deep Learning Models for EEG Classification](https://mlsanity.com/paper/2012.01074/) (2020)
- [Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs](https://mlsanity.com/paper/2105.06512/) (2021)
- [The Benefit of the Doubt: Uncertainty Aware Sensing for Edge Computing Platforms](https://mlsanity.com/paper/2102.05956/) (2021)

## Built on by (most cited first, 1 of 1)

- [Energy-Efficient Tree-Based EEG Artifact Detection](https://mlsanity.com/paper/2204.09577/) (2022)

## Abstract (arXiv)

Electroencephalography (EEG) is crucial for the monitoring and diagnosis of brain disorders. However, EEG signals suffer from perturbations caused by non-cerebral artifacts limiting their efficacy. Current artifact detection pipelines are resource-hungry and rely heavily on hand-crafted features. Moreover, these pipelines are deterministic in nature, making them unable to capture predictive uncertainty. We propose E4G, a deep learning framework for high frequency EEG artifact detection. Our framework exploits the early exit paradigm, building an implicit ensemble of models capable of capturing uncertainty. We evaluate our approach on the Temple University Hospital EEG Artifact Corpus (v2.0) achieving state-of-the-art classification results. In addition, E4G provides well-calibrated uncertainty metrics comparable to sampling techniques like Monte Carlo dropout in just a single forward pass. E4G opens the door to uncertainty-aware artifact detection supporting clinicians-in-the-loop frameworks.