EEG Denoising Model Training for Device Noise Removal
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Solution Overview
Problem
EEG signals are easily interfered with by noise from the collection device and other sources, necessitating a method to effectively denoise the signals for accurate analysis.
Innovation Solution
A method and system for training an EEG signal denoising model using a denoising model trained with collected EEG signals, incorporating a discriminant model to adjust model parameters based on discrimination results, effectively removing noise from the EEG signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EEG signals are collected using an EEG collection device, then the EEG signals can be obtained for analysis, but the collected signals contain noise from the device itself and other sources that interfere with accurate analysis
Solution Approach 1:
The patent introduces a denoising model as an intermediary component between the EEG collection device and the analysis system. This model processes the noisy collected signals and outputs cleaned signals, effectively mediating the harmful noise interference while preserving the useful EEG information for accurate analysis
Solution Approach 2:
The patent replaces traditional mechanical or physical noise filtering methods with an intelligent denoising model based on machine learning. Instead of using hardware filters or manual processing, the system uses a trained neural network model to automatically identify and remove noise from EEG signals
2Reliability
If traditional denoising methods are used on EEG signals, then some noise can be removed, but the denoising effectiveness is limited and cannot specifically address noise from the collection device
Solution Approach 1:
The patent applies local quality by training the denoising model specifically on noise characteristics from a particular EEG collection device. The model learns device-specific noise patterns and applies targeted denoising strategies tailored to that specific device's noise profile, rather than using generic denoising approaches
Solution Approach 2:
The patent implements feedback mechanisms where the denoising model's output is evaluated and used to refine its performance. The model continuously learns from the difference between denoised signals and ground truth, adjusting its parameters to improve denoising effectiveness for device-specific noise patterns
Data Source
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AI summary
A method and system for training an EEG signal denoising model of an EEG collection device (120) is provided. The method includes generating an analog EEG signal (110) and obtaining a collected EEG signal by collecting the analog EEG signal (110) through the EEG collection device (120). The collected EEG signal contains a noise caused by the EEG collection device (120). The method further includes obtaining the EEG signal denoising model of the EEG collection device (120) by training a denoising model (140) based on the collected EEG signal. The training includes obtaining a denoised EEG signal (150) by processing the collected EEG signal through the denoising model (140); obtaining a discrimination result by processing the denoised EEG signal (150) through a discriminant model; and adjusting a model parameter of the denoising model (140) based on the discrimination result. The discrimination result reflects a probability that the discriminant model determines that the processed denoised EEG signal is a clean EEG signal.