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EKFNet: edge-based Kalman filter network for real-time EEG signal denoising
Jiaquan Yan1, Zhuoli He1,2, Naveed Ur Rehman Junejo3,4
1Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, School of Computer and Big Data, Minjiang University, Fuzhou 350121, People's Republic of China.
This study introduces an edge-based lightweight Kalman filter network (EKFNet) for denoising electroencephalogram (EEG) signals on portable devices. The EKFNet significantly improves signal quality and reduces computational load, enabling efficient deployment on wearable electronics.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Deep Learning
Background:
- Deep learning methods for electroencephalogram (EEG) signal denoising are computationally intensive.
- Existing models face deployment challenges on edge-based portable or wearable (P/W) electronics.
- High computational complexity limits the application of advanced denoising techniques on resource-constrained devices.
Purpose of the Study:
- To propose an edge-based lightweight Kalman filter network (EKFNet) for efficient EEG signal denoising.
- To develop a deep learning model that eliminates the need for manual prior knowledge estimation.
- To enable the deployment of advanced signal denoising on portable and wearable devices.
Main Methods:
- Constructed a multi-scale feature fusion module to capture and implicitly compute prior knowledge.
- Designed an adaptive gain estimation module using LSTM and sequential channel attention for dynamic Kalman gain prediction.
- Implemented an optimization strategy with operator fusion and constant folding to reduce computational overhead and memory footprint.
Main Results:
- EKFNet reduced the sum of the square of the distances by at least 12% compared to state-of-the-art methods.
- Improved cosine similarity by at least 2.2% over existing denoising techniques.
- Model optimization achieved an approximate 3.3× reduction in inference time.
Conclusions:
- The EKFNet effectively integrates Kalman filtering with deep learning for EEG signal denoising.
- This approach overcomes parameter-setting challenges of traditional algorithms while minimizing computational and memory requirements.
- EKFNet offers a favorable balance between algorithmic performance and computing power for edge device applications.
