E2CAR: An Efficient 2D-CNN Framework for Real-Time EEG Artifact Removal on Edge Devices
This work addresses the need for efficient EEG signal processing on edge devices, representing an incremental improvement over existing methods.
The paper tackled the problem of computationally expensive EEG artifact removal for real-time edge applications by proposing an Efficient 2D-CNN framework, achieving a 90% reduction in inference time and an 18.98% decrease in power consumption while maintaining comparable performance.
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.