Human-Centric Lighting Spectral Tuning for Emotion Detection
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Solution Overview
Problem
Current methods for human-centric lighting systems are unable to effectively adjust lighting parameters to induce specific emotions due to the high cost and inaccessibility of fMRI systems, and the variability of brainwave patterns across individuals, making it impossible to use fMRI or EEG alone for commercial applications.
Innovation Solution
Establishing a correlation between EEG brainwave patterns and fMRI's blood oxygen-level dependent (BOLD) responses to create a method for adjusting lighting parameters, using a cloud database and intelligent communication devices to select and adjust spectral recipes based on emotional responses, and employing a system with multi-spectral light-emitting devices and ambient light sensors to achieve desired emotional states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fMRI system is used to distinguish emotions by blood oxygen-level dependent response, then measurement precision is improved, but device complexity and cost increase significantly making it unusable for commercial applications
Solution Approach 1:
The patent creates a simplified copy of the fMRI measurement capability by establishing a correlation model between EEG brainwave patterns and fMRI BOLD responses. Instead of using the complex fMRI system directly, the invention uses EEG to capture brainwave patterns and maps them to emotional states through a pre-established correlation with fMRI data, thereby achieving similar emotion detection functionality with much lower complexity and cost
Solution Approach 2:
The patent introduces EEG technology as an intermediary between the complex fMRI system and the final emotion detection application. The EEG system serves as a mediator that captures brainwave patterns which are then correlated with fMRI BOLD responses through a predetermined model, enabling emotion detection without directly using the complex fMRI hardware
2Device complexity
If only EEG brainwave patterns are used to judge emotions, then device complexity is reduced, but reliability decreases due to individual variability in brainwave patterns
Solution Approach 1:
The patent performs preliminary action by pre-establishing a correlation model between EEG brainwave patterns and fMRI BOLD responses before actual emotion detection. This correlation model is created in advance using data from multiple individuals, capturing the relationship between different brainwave patterns and emotional states. When performing actual emotion detection, the system simply applies this pre-established model to the individual's EEG data, thereby overcoming individual variability without requiring complex real-time adjustments
Solution Approach 2:
The patent changes the parameter basis for emotion detection by transitioning from raw EEG brainwave patterns alone to a transformed parameter set that incorporates the correlation with fMRI BOLD responses. By applying the predetermined correlation model, the system transforms individual EEG data into a standardized emotional state classification that accounts for individual variability, thereby improving reliability while maintaining low device complexity
3Adaptability or versatility
If spectral recipes are customized for each individual based on their emotional responses, then adaptability is improved, but loss of time increases due to individualized calibration
Solution Approach 1:
The patent applies universality by creating a standardized spectral recipe framework that can be applied across multiple users. The system establishes a correlation model that works for different individuals by mapping their EEG patterns to emotional states through a common reference framework. This allows the same spectral recipe structure to serve multiple users with different emotional responses, reducing the need for completely individualized calibration while still maintaining adaptability through parameter adjustments within the universal framework
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 the creation of a commercially viable human-centric lighting system that can effectively induce specific emotions by correlating EEG patterns with fMRI responses, reducing costs and individual variability, and providing a platform for sharing and customizing spectral recipes for emotional lighting.
Implementation Method 1
start the light-emitting device with the lighting parameters in the selected specific spectral recipe to emit a specific color temperature of multi spectrum
Data Source
AI summary
The present invention is a human centric lighting (HCL) method with adjustable lighting parameters comprises the following steps: 1) User connects with the cloud through the intelligent communication device and selects the specific spectral recipe that the user wants to achieve a specific emotion from the cloud; 2) The light emitting device is configured to emit light in a specific light field according to the lighting parameters in a specific light field to select the light in a specific light field; 3) After the user performs HCL, when the effect of specific emotion is not reached, the user adjusts the lighting parameters in the selected specific spectral recipe through the intelligent communication device; 4) When the user has achieved the effect of specific emotion after performing HCL, store the corresponding lighting parameters of the adjusted spectral recipe to the cloud.