Brainwave Signal Denoising via Wavelet Neural Compression
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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 suitable for complex signals like MEG and EEG data.
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 clean signals in the time domain, effectively handling signals with unknown features by segmenting noise and using self-supervised learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised neural network training is used for signal denoising, then signal extraction accuracy is improved for known signal types, but the method becomes inapplicable to signals with unknown or unspecific behavior patterns
Solution Approach 1:
The patent employs self-supervised learning where the neural network learns to reconstruct clean signals from noisy inputs without requiring externally provided clean signal labels. The network serves itself by using its own internal representations to guide the learning process, enabling it to handle unknown signal types while maintaining extraction accuracy
Solution Approach 2:
The patent segments the signal processing task into multiple stages: initial noise reduction, feature extraction, and final signal reconstruction. This segmentation allows the system to progressively refine the signal without requiring complete prior knowledge of the signal characteristics, making it adaptable to both known and unknown signal types
2Object-affected harmful factors
If traditional preprocessing methods are applied to noisy signals, then some noise is removed, but valuable signal information is also lost
Solution Approach 1:
The patent transforms the signal from the time domain to the frequency domain using Fourier transform, allowing selective manipulation of signal parameters. By operating in the frequency domain, the system can target specific noise frequencies without affecting the underlying signal frequencies, thus reducing noise while preserving signal information
Solution Approach 2:
The patent introduces an intermediate representation of the signal through frequency domain transformation and neural network processing. This intermediate form allows the system to separate noise from signal more effectively before reconstructing the final clean signal, preventing information loss that would occur with direct time-domain filtering
3Reliability
If the entire signal is used for neural network training at once, then comprehensive learning is achieved, but hardware complexity increases
Solution Approach 1:
The patent divides the training signal into smaller segments or batches that can be processed sequentially or in parallel with reduced memory requirements. This segmentation maintains the comprehensiveness of learning by ensuring all signal portions are eventually processed, while reducing the peak memory and computational demands on hardware
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 nonintrusive 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.