Human-Centric Lighting Emotion Navigation Using EEG-fMRI Mapping
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Solution Overview
Problem
Current methods for determining human emotions using functional Magnetic Resonance Imaging (fMRI) and Electroencephalography (EEG) are expensive and impractical for commercial human-centric lighting systems, and EEG alone is unreliable due to varying brainwave patterns across individuals, preventing effective emotion-based lighting solutions.
Innovation Solution
Establish a correlation between EEG brainwave patterns and fMRI's blood oxygen-level dependent (BOLD) contrast responses to develop a multispectral lighting recipe for emotion navigation, using a cloud-connected intelligent lighting system to adjust lighting parameters like color temperature, illuminance, and flicker frequency based on user emotions.
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 making it impractical for commercial use
Solution Approach 1:
The patent creates a mapping relationship between EEG brainwave patterns and fMRI BOLD contrast responses by having subjects experience multiple emotions while undergoing both fMRI and EEG examinations. This establishes a correspondence model where EEG patterns can infer fMRI responses, allowing the system to use the simpler EEG device while achieving measurement precision comparable to fMRI through the established correlation model
Solution Approach 2:
The patent introduces a correlation model as an intermediary between EEG and fMRI measurements. This model, built through simultaneous fMRI-EEG examination during emotion induction, acts as a translator that converts EEG brainwave patterns into inferred BOLD contrast responses, enabling the use of affordable EEG equipment while achieving fMRI-level emotion determination accuracy
2Ease of manufacture
If EEG is used to determine emotions through brainwave patterns, then device cost is reduced, but reliability deteriorates due to varying brainwave patterns across different individuals
Solution Approach 1:
The patent performs preliminary calibration for each user by conducting fMRI and EEG examinations while inducing multiple emotions beforehand. This establishes a personalized mapping relationship between EEG brainwave patterns and emotional states for each individual, storing the calibrated data for later use. When determining emotions in practical applications, the system uses this pre-established personalized model to accurately interpret EEG patterns, eliminating reliability issues caused by inter-individual variations
Solution Approach 2:
The patent implements a dynamic, adaptive system where the EEG-emotion mapping model is personalized for each user through preliminary calibration. The system adjusts to individual differences by creating unique correlation models during initial fMRI-EEG combined examinations, then uses these customized models for reliable emotion determination in subsequent applications, making the reliability adaptable to each user's unique brainwave characteristics
3Reliability
If personalized calibration for each user is implemented, then emotion determination reliability is improved, but loss of time increases due to additional calibration procedures
Solution Approach 1:
The patent performs the time-consuming fMRI-EEG calibration process in advance during an initial examination session, storing the established mapping relationship for future use. Once calibrated, the system can rapidly determine emotions using only EEG in practical applications without requiring repeated fMRI scans or recalibration, thus converting initial time investment into long-term efficiency gains
4Measurement precision
If fMRI and EEG are combined to determine emotions, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent performs combined fMRI-EEG examination during emotion induction to establish a mapping relationship between the two measurement modalities. After this preliminary calibration phase, the system uses only EEG for emotion determination in practical applications, leveraging the pre-established correlation to achieve fMRI-level accuracy without the ongoing complexity and cost of simultaneous fMRI-EEG operation
Solution Approach 2:
The patent creates a virtual model of fMRI BOLD contrast responses based on EEG measurements through established correlation. This allows the system to infer what fMRI would detect using only EEG hardware, effectively copying fMRI's measurement capabilities through a simpler device via the learned mapping relationship
Data Source
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AI summary
The present invention discloses a method for implementing emotion navigation by using an intelligent human centric lighting system. The emotion navigation method includes the following steps. Step 1 is to confirm an initial emotion of a user. Step 2 is to set a target emotion. Step 3 is to select a relay emotion to set an emotion navigation path. Step 4 is to edit a multispectral recipe corresponding to the relay emotion and the target emotion according to the multispectral recipe. Step 5 is to perform a lighting program of the multispectral recipe of the relay emotion and the target emotion. If the determination is not reaching the target emotion of Step 6, then go to obtain the user's personal physiological data to find out the multispectral recipe of the corresponding emotion that can adjust to the target emotion.