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

TL;DR

This paper introduces E2CAR, a 2D-CNN framework optimized for real-time EEG artifact removal on edge devices, significantly reducing inference time and power consumption while maintaining performance.

Contribution

It proposes a novel 2D-CNN based framework, E2CAR, optimized for edge hardware, achieving substantial efficiency improvements over traditional methods.

Findings

90% reduction in inference time on TPU

18.98% decrease in power consumption

Maintains comparable artifact removal performance

Abstract

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.