Braincap Signal Denoising via Wavelet Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current signal processing methods are inadequate for extracting signals of unknown or unspecified form from severe noise corruption, particularly in medical diagnostics, as they require pre-known signal behavior for effective denoising and are not applicable to complex signals like MEG and EEG, which lack theoretical expected behavior.
Innovation Solution
A signal processing method using multi-scale decomposition and discrete wavelet transforms, combined with auto-associative neural networks, to decompose, compress, and reconstruct signals in the wavelet domain, effectively separating noise from clean signals without prior knowledge of the signal's behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal processing methods are used for denoising, then signals with known theoretical behavior can be effectively processed, but signals of unknown or unspecified form cannot be properly denoised
Solution Approach 1:
The auto-associative neural network performs self-supervised learning by training the network to reproduce its input at the output. The network learns to identify and preserve signal components while suppressing noise through its internal weight adjustments, without requiring external training data or prior knowledge of signal characteristics. This self-service mechanism enables the system to adapt to any signal form automatically.
Solution Approach 2:
The system transforms the signal from the time domain to the wavelet domain, changing the representation parameters of the signal. This parameter transformation allows the neural network to operate on transformed coefficients where signal and noise have different characteristics, enabling effective separation without prior knowledge of the original signal form.
2Reliability
If supervised neural network training is used, then signals with known expected behavior can be denoised effectively, but signals without theoretical expected behavior cannot be processed
Solution Approach 1:
The system employs self-supervised learning where the neural network is trained to reproduce its own input signal at the output. The network learns to distinguish signal from noise by minimizing the error between input and output, automatically adapting to the specific characteristics of each signal without requiring pre-labeled training data or theoretical models of the signal behavior.
Solution Approach 2:
Instead of trying to directly model the signal characteristics, the system inverts the approach by training the network to preserve what it receives (the noisy signal) while suppressing deviations. The network learns by minimizing the difference between input and output, effectively identifying and preserving signal components while removing noise through this inverted training paradigm.
3Measurement precision
If wavelet decomposition and compression are applied, then noise can be squeezed out in the transform domain, but the system complexity increases
Solution Approach 1:
The system segments the signal processing task into distinct stages: wavelet decomposition into multiple scales, independent processing of each scale's coefficients through the neural network, and reconstruction. This segmentation allows the complex denoising problem to be broken down into simpler, parallel processing steps that can be handled more efficiently.
Solution Approach 2:
The wavelet transform acts as an intermediary that transforms the noisy signal into a different domain (wavelet coefficients) where the neural network can more effectively separate signal from noise. This intermediary transformation simplifies the denoising task by representing the signal in terms that are more amenable to compression-based noise removal.
Data Source
AI summary
A signal processing method and system combines multi-scale decomposition, such as wavelet, pre-processing together with a compression technique, such as an auto-associative artificial neural network, operating in the multi-scale decomposition domain for signal denoising and extraction. All compressions are performed in the decomposed domain. A reverse decomposition such as an inverse discrete wavelet transform is performed on the combined outputs from all the compression modules to recover a clean signal back in the time domain. A low-cost, non-drug, non-invasive, on-demand therapy braincap system and method are pharmaceutically non-intrusive to the body for the purpose of disease diagnosis, treatment therapy, and direct mind control of external devices and systems. It is based on recognizing abnormal brainwave signatures and intervenes at the earliest moment, using magnetic and/or electric stimulations to reset the brainwaves back to normality. The feedback system is self-regulatory and the treatment stops when the brainwaves return to normal. The braincap contains multiple sensing electrodes and microcoils; the microcoils are pairs of crossed microcoils or 3-axis triple crossed microcoils.