Multimodal Biometric Headset Signal Processing
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Solution Overview
Problem
Traditional non-invasive plethysmography systems face limitations in measuring PPG signals, particularly with reflective type sensors, due to issues like signal quality analysis, skin tone variations, and motion artifacts, which affect signal-to-noise ratio and data accuracy.
Innovation Solution
The development of a headset with capacitive non-dermal EEG electrodes and PPG sensors that apply automatic gain control and adaptive noise cancellation techniques, using discrete cosine and Hilbert transforms over a reduced frequency range to enhance signal quality and reduce motion artifacts, while being adjustable for different skin tones and sensor placements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional PPG signal quality analysis methods (perfusion index, skewness, regression) are used, then signal quality can be analyzed, but the methods are limited for reflective type PPG sensors and not comparable across different skin tones
Solution Approach 1:
The patent transforms PPG signal quality analysis from traditional time-domain parameters (perfusion index, skewness) to frequency-domain parameters using Discrete Cosine Transform (DCT) and Hilbert Transform. This parameter transformation enables accurate signal quality assessment for reflective type PPG sensors across different skin tones by analyzing spectral characteristics rather than amplitude-based metrics that vary with skin pigmentation.
Solution Approach 2:
The patent replaces traditional signal processing approaches with advanced mathematical transforms (DCT and Hilbert Transform) to extract signal quality metrics. This substitution allows for more robust signal analysis that is independent of sensor type and skin tone variations, as frequency-domain analysis captures essential signal characteristics without being influenced by amplitude attenuation due to melanin absorption.
2Measurement precision
If automatic gain control and adaptive noise cancellation are applied, then signal quality and noise reduction are improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies Discrete Cosine Transform (DCT) and Hilbert Transform to pre-process PPG signals before quality analysis. By performing these transforms upfront, the system extracts essential spectral and envelope information that simplifies subsequent signal quality assessment and noise cancellation, reducing the computational burden of later processing stages.
Solution Approach 2:
The patent implements adaptive noise cancellation using feedback mechanisms where signal quality metrics derived from DCT and Hilbert Transform analysis inform gain control and filtering adjustments. This feedback loop continuously optimizes signal quality while managing computational complexity through adaptive rather than exhaustive processing.
3Productivity
If DCT and Hilbert transform are computed over reduced frequency range, then processing speed is improved without lag, but frequency resolution may be reduced
Solution Approach 1:
The patent computes DCT and Hilbert Transform over a reduced frequency range specifically tailored to the PPG signal spectrum (focusing on cardiac and respiratory frequency bands). This parameter optimization achieves real-time processing speed by concentrating computational resources on the most relevant frequency components where physiological information resides, rather than analyzing the entire frequency spectrum.
Solution Approach 2:
The patent applies frequency-range optimization by analyzing only the specific frequency bands relevant to PPG signals (typically 0.5-10 Hz for cardiac and respiratory rates). This local quality approach maintains sufficient frequency resolution for physiological measurement while dramatically reducing computational load compared to full-spectrum analysis, achieving the right balance between speed and precision for the application.
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
Enables real-time, accurate measurement of EEG and PPG signals with improved signal quality and reduced noise, allowing for effective brain activity detection and heart rate/respiration rate estimation without lag, suitable for various skin tones and motion conditions.
Implementation Method 1
real-time measuring of electroencephalography (EEG), photoplethysmography (PPG), and inertial measurement unit (IMU) signals from locations on the forehead and head
Implementation Method 2
measuring of PPG signals from a forehead location... The sensor placement may identify one or more optimal locations for determining PPG signals
Implementation Method 3
Brain waves can be detected via EEG, which involves monitoring and recording electrical impulse activity of the brain
Implementation Method 4
The electrodes detect the electrical impulses generated by the brain and send signals to a computer that records the results
Implementation Method 5
inertial measurement unit (IMU) signals from locations on the forehead and head in the detection of brain activity
Data Source
AI summary
A headset and methods for using the headset to collect EEG data, PPG data and IMU data are disclosed. Raw EEG data generated from the headset (or other device) may be received. The EEG data may be run through spectral analysis to isolate various spectral components in each channel, isolating the brain wave components for each channel. Similar data for heart rate, respiratory rate and heart rate variability can be extrapolated from PPG data as well as the positional movements in space along with acceleration and angular velocity may be determined from IMU data. A visual display may be generated based on the isolated components.