Dynamic Spectrum Editing for EEG-Guided Human-Centric Lighting
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Solution Overview
Problem
Current methods using functional Magnetic Resonance Imaging (fMRI) and electroencephalograph (EEG) systems are expensive and impractical for commercial human-centric lighting systems, as they cannot reliably correlate brainwave patterns with blood oxygen-level dependent (BOLD) contrast responses to determine emotions accurately.
Innovation Solution
Establish a correlation between EEG brainwave patterns and fMRI BOLD responses to develop a dynamic spectrum program for human-centric lighting, using a cloud-based software as a service (SaaS) system to create situational dynamic spectrum programs that adjust lighting parameters based on emotional responses, and utilize a cloud database to store and adjust lighting parameters for personalized emotional experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fMRI system is used to determine emotions through BOLD contrast response, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent creates a simplified copy of the fMRI emotion detection capability using EEG technology. By training an AI model on fMRI data to learn the mapping between brain states and emotions, the system replicates fMRI's measurement precision using the simpler and cheaper EEG device, thus resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent replaces the complex mechanical fMRI scanning system with an EEG-based computational system. Instead of using expensive magnetic resonance imaging hardware, the invention uses electrical signal measurement combined with AI algorithms to achieve equivalent emotion detection capability, substituting mechanical complexity with computational intelligence
2Device complexity
If EEG is used to determine emotions through brainwave patterns, then device complexity is reduced, but measurement precision deteriorates due to individual variations
Solution Approach 1:
The patent implements feedback through AI model training where the system continuously learns from labeled emotion data. The AI model adjusts its parameters based on feedback from training examples, improving its ability to accurately classify emotions from EEG signals despite individual variations, thus resolving the precision issue while maintaining device simplicity
Solution Approach 2:
The patent transforms the raw EEG parameters into emotionally relevant features through AI processing. By changing the parameter representation from raw brainwave signals to AI-extracted emotional indicators, the system achieves accurate emotion detection while keeping the EEG device simple and inexpensive
3Measurement precision
If fMRI system is deployed for commercial human centric lighting, then measurement precision is improved, but loss of energy and operational cost increase
Solution Approach 1:
The patent replaces expensive, energy-intensive fMRI systems with inexpensive, low-power EEG devices. The EEG devices consume minimal energy compared to fMRI scanners, making them suitable for commercial deployment in human centric lighting applications while maintaining acceptable measurement precision through AI enhancement
4Device complexity
If EEG alone is used without fMRI correlation, then device complexity is reduced, but reliability deteriorates due to inability to correlate with BOLD response
Solution Approach 1:
The patent performs preliminary action by pre-training the AI model on fMRI data before actual emotion detection. This preliminary training establishes the correlation between EEG patterns and emotions based on fMRI-validated ground truth, ensuring reliability is built into the system beforehand while keeping the operational system simple and EEG-based
Data Source
AI summary
The present invention relates to an editing method of a dynamic spectrum program, which includes the following steps. Downloading the spectral recipe is to download the spectral recipe with multi light scene with specific color temperature or at least one specific color temperature to the editing device through the internet, and connect the editing device with the software as a service system or platform as a service system configured in the cloud. Perform the correlation editing between the specific function and the spectral recipe. The editing device divides the specific function to be achieved into multiple blocks through the software as a service system or the platform as a service system, and the blocks correspond to the spectral recipe of the multi light scene respectively. And forming a functional dynamic spectrum program, which is to configure multiple blocks for corresponding time to form a dynamic spectrum program.