Wavelet Packet Atom Reordering for EEG Brain Activity Interpretation
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Solution Overview
Problem
Current methods for monitoring brain activity, such as EEG and fMRI, face limitations in spatial resolution and practicality for continuous, non-clinical use, particularly in detecting specific brain states and neurological conditions.
Innovation Solution
A computer-implemented method using real-time processing of electrical signal data from the brain, employing mother wavelets, wavelet packet atoms, and normalization factors to deconstruct and reorder brain activity features, enabling the identification of personalized mental states and neurological conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If EEG electrodes are placed along the scalp for noninvasive monitoring, then ease of operation is improved, but measurement precision deteriorates due to limited spatial resolution
Solution Approach 1:
The patent segments the brain activity monitoring task by decomposing EEG signals into multiple frequency bands using wavelet packet transformation. This allows different regions of the brain to be monitored independently at different temporal and frequency resolutions, effectively overcoming the limited spatial resolution of scalp electrodes while maintaining noninvasive operation.
Solution Approach 2:
The patent introduces temporal and frequency dimensions to compensate for the limited spatial resolution. By transforming EEG signals into the time-frequency domain using wavelet packets, the system can localize brain activity not just spatially but also temporally and spectrally, providing multi-dimensional information that overcomes the constraints of scalp electrode placement.
2Measurement precision
If fMRI is used to monitor brain activity, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent replaces the complex mechanical and electromagnetic systems of fMRI with a computational approach using wavelet packet transformation and machine learning algorithms. Instead of requiring expensive magnetic resonance imaging hardware, the system uses software-based signal processing to achieve similar or superior measurement precision for brain state detection.
Solution Approach 2:
The patent creates a computational model that copies and processes EEG signal patterns to infer brain states. By training machine learning algorithms on EEG data patterns associated with specific brain states, the system can accurately replicate and predict brain activity without needing the complex hardware infrastructure of fMRI scanners.
3Measurement precision
If fMRI is used for brain monitoring, then measurement precision is improved, but portability and continuous use are limited
Solution Approach 1:
The patent replaces expensive, bulky fMRI equipment with inexpensive, portable EEG electrodes that can be easily placed on the scalp. These simple, disposable-like EEG sensors enable continuous brain monitoring in various settings including homes and mobile environments, eliminating the portability constraints of fMRI while maintaining adequate measurement precision through advanced signal processing.
4Ease of operation
If EEG is used to detect brain activity, then ease of operation is improved, but reliability deteriorates due to sensitivity limits
Solution Approach 1:
The patent applies preliminary signal processing actions including wavelet packet decomposition and feature extraction before final brain state classification. By pre-processing the EEG signals to remove artifacts, normalize data, and extract meaningful features in advance, the system enhances detection reliability while maintaining the ease of noninvasive EEG operation.
Solution Approach 2:
The patent implements feedback mechanisms where machine learning algorithms continuously learn from and adjust to EEG signal patterns. The system uses feedback from classification results to refine future detections, improving reliability over time while maintaining the simplicity of EEG electrode placement and operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a non-invasive, efficient means to detect and diagnose neurological conditions, including Alzheimer's, anxiety, and sleep disorders, by offering real-time visualization of brain activity, facilitating effective therapy monitoring and dosing regimen determination.
Implementation Method 1
employing mother wavelets, wavelet packet atoms, and normalization factors to deconstruct and reorder brain activity features
Data Source
AI summary
A method includes receiving electroencephalographic (EEG) signal data recordings collected from a plurality of individuals via at least one EEG monitoring device. An optimized plurality of wavelet packet atoms is constructed based on the EEG signal data recordings and a mother wavelet. The optimized plurality of wavelet packet atoms is reordered to obtain an optimal reordered set of wavelet packet atoms. The optimal reordered set of wavelet packet atoms is normalized to obtain an optimal normalized set of wavelet packet atoms that is representative of brain activities of the plurality of individuals. A particular EEG signal data recording of a particular individual is received, which is projected onto the optimal normalized set of wavelet packet atoms to obtain an individual-specific set of projections for the particular individual on the optimal normalized set of wavelet packet atoms. A brain activity representation of the particular individual is generated based on the individual-specific set of projections.