EEG Signal Denoising via Wavelet Packet Decomposition
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Solution Overview
Problem
Existing methods for improving the signal-to-noise ratio of EEG signals, particularly the P300 response, are ineffective due to their reliance on static noise filters that fail to adapt to varying noise patterns over time, resulting in minimal improvement across the 300-600 millisecond duration.
Innovation Solution
A processor-implemented method using wavelet packet decomposition to dynamically adjust noise filtering for each time segment of the EEG signal, setting default and update signals, and recalculating coefficients to retain only nodes that improve the signal-to-noise ratio, effectively reconstructing a de-noised waveform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a static noise filter is applied to sequential time segments of the EEG signal, then the signal to noise ratio is improved over a portion of the time period, but minimal improvement is achieved over other portions due to varying noise patterns
Solution Approach 1:
The patent transforms the static noise filter into a dynamic system by continuously updating filter coefficients based on the actual noise characteristics detected in each time segment. The filter adapts its parameters in real-time to match the varying noise patterns across different portions of the EEG signal, thereby maintaining optimal signal-to-noise ratio improvement throughout the entire time period.
Solution Approach 2:
The invention changes the parameters of the noise filter dynamically by calculating and applying different filter coefficients for different time segments. Instead of using fixed filter parameters, the system computes parameters that specifically match the noise characteristics of each segment, enabling the filter to effectively handle the time-varying nature of EEG noise.
2Measurement precision
If wavelet packet decomposition with multiple nodes is used to filter noise, then frequency band separation is achieved, but computational complexity increases
Solution Approach 1:
The patent applies wavelet packet decomposition to segment the EEG signal into multiple frequency bands, allowing independent analysis and filtering of each band. This segmentation enables precise frequency band separation while managing computational complexity by processing each segment separately rather than analyzing the entire signal at once.
Solution Approach 2:
The invention extracts and removes noise components from specific frequency bands by identifying and eliminating unwanted wavelet packet nodes. By taking out only the noisy portions from specific frequency segments rather than processing the entire signal uniformly, the method achieves effective noise reduction with reduced computational burden.
Data Source
AI summary
A method for improving the signal to noise ratio of an EEG signal in which a wavelet packet decomposition having a plurality of levels is first applied to a time slice of the EEG signal. A default signal is set to the first wavelet packet and a default peak response is then calculated for the first wavelet node. An update signal is set to the default signal combined with another of the wavelet nodes and an update peak response signal is then calculated of the update signal. If the update peak response signal exceeds the default peak response, the default peak response is set equal to the update peak response and the default signal is set equal to the update signal. Otherwise, the value of the current node is set to zero which effectively eliminates the signal data of the current wavelet node. These steps are reiterated for all of the wavelet nodes and, thereafter, a composite waveform of the EEG signal is reconstructed from the non-zero wavelet nodes.