E2CAR: An Efficient 2D-CNN Framework for Real-Time EEG Artifact Removal on Edge Devices
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915787014406144 |
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| author | Liu, Haoliang Cai, Chengkun Zhao, Xu Li, Lei |
| author_facet | Liu, Haoliang Cai, Chengkun Zhao, Xu Li, Lei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_09035 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | E2CAR: An Efficient 2D-CNN Framework for Real-Time EEG Artifact Removal on Edge Devices Liu, Haoliang Cai, Chengkun Zhao, Xu Li, Lei Signal Processing Artificial Intelligence 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. |
| title | E2CAR: An Efficient 2D-CNN Framework for Real-Time EEG Artifact Removal on Edge Devices |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2602.09035 |