# E2CAR: An Efficient 2D-CNN Framework for Real-Time EEG Artifact Removal on Edge Devices

arXiv: https://arxiv.org/abs/2602.09035 · HTML: https://mlsanity.com/paper/2602.09035/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising](https://mlsanity.com/paper/2009.11662/) (2020)
- [Scaling Law for Time Series Forecasting](https://mlsanity.com/paper/2405.15124/) (2024)
- [Human Motion Instruction Tuning](https://mlsanity.com/paper/2411.16805/) (2024)
- [DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devices](https://mlsanity.com/paper/2108.09457/) (2021)
- [The Role of Deductive and Inductive Reasoning in Large Language Models](https://mlsanity.com/paper/2410.02892/) (2024)
- [Bayesian Optimization for Controlled Image Editing via LLMs](https://mlsanity.com/paper/2502.18116/) (2025)
- [Attention Consistency for LLMs Explanation](https://mlsanity.com/paper/2509.17178/) (2025)
- [Deep Learning in the Era of Edge Computing: Challenges and Opportunities](https://mlsanity.com/paper/2010.08861/) (2020)
- [Deep learning denoising for EOG artifacts removal from EEG signals](https://mlsanity.com/paper/2009.08809/) (2020)

## Abstract (arXiv)

Electroencephalography (EEG) signals are frequently contaminated by artifacts, affecting the accuracy of subsequent analysis. Traditional artifact removal methods are often computationally expensive and inefficient for real-time applications in edge devices. This paper presents a method to reduce the computational cost of most existing convolutional neural networks (CNN) by replacing one-dimensional (1-D) CNNs with two-dimensional (2-D) CNNs and deploys them on Edge Tensor Processing Unit (TPU), which is an open-resource hardware accelerator widely used in edge devices for low-latency, low-power operation. A new Efficient 2D-CNN Artifact Removal (E2CAR) framework is also represented using the method above, and it achieves a 90% reduction in inference time on the TPU and decreases power consumption by 18.98%, while maintaining comparable artifact removal performance to existing methods. This approach facilitates efficient EEG signal processing on edge devices.