Lighting Scene Sharing Platform Using EEG-fMRI Emotion Mapping
Find Innovative SolutionsGenerate Solutions
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 accurately determine emotional states due to varying brainwave patterns and high costs.
Innovation Solution
Establish a correlation between EEG brainwave patterns and fMRI's blood oxygen-level dependent (BOLD) contrast responses to create a grading method for emotional responses, using intelligent human-centric lighting systems with multispectral light-emitting devices, and construct a spectral recipe database for effective emotional stimulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fMRI system is used to determine emotional states, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent creates a simplified copy of the fMRI measurement capability using EEG technology. By establishing a correlation model between EEG brainwave patterns and fMRI BOLD contrast responses, the system replicates emotional state detection functionality with a less complex, more cost-effective device that maintains measurement precision through the learned mapping relationships.
Solution Approach 2:
The patent replaces the complex mechanical and hardware infrastructure of fMRI systems with a software-based correlation model running on standard computing platforms. The emotional state detection function is substituted from a physical imaging system to an algorithmic processing system that uses EEG data and pre-trained models to achieve similar measurement precision without the associated complexity.
2Measurement precision
If fMRI system is used to determine emotional states, then measurement precision is improved, but cost increases significantly
Solution Approach 1:
The patent replaces expensive, long-term infrastructure (fMRI systems costing millions) with affordable, disposable-like EEG sensors and software licenses that can be deployed at minimal cost. The correlation model serves as a virtual replacement that eliminates the need for costly physical imaging equipment while maintaining measurement precision through algorithmic processing of cheaper EEG data.
3Device complexity
If EEG is used to determine emotional states, then device complexity is reduced, but measurement precision deteriorates due to varying brainwave patterns
Solution Approach 1:
The patent performs preliminary training actions by collecting and processing paired EEG-fMRI data from multiple subjects to establish correlation models before actual emotional state detection. This pre-training phase creates subject-specific or population-level mapping relationships that compensate for the natural variability in brainwave patterns, enabling accurate emotional state determination from EEG data without requiring complex real-time adjustments.
Solution Approach 2:
The patent introduces a correlation model as an intermediary between raw EEG signals and emotional state conclusions. This intermediate processing layer translates variable brainwave patterns into standardized emotional state predictions by referencing pre-established relationships between EEG features and fMRI-validated emotional states, thereby maintaining measurement precision despite EEG's inherent variability.
4Ease of operation
If EEG is used instead of fMRI, then ease of operation is improved, but measurement precision worsens
Solution Approach 1:
The patent creates a functional copy of fMRI's emotional state detection capability using EEG technology combined with correlation models. This copied functionality maintains the measurement precision of fMRI while achieving the ease of operation of EEG, as the system processes simple EEG signals through software algorithms to produce fMRI-level emotional state accuracy without requiring complex imaging equipment or procedures.
Data Source
AI summary
A method for constructing a lighting environment sharing platform includes constructing a multi-spectrum light-emitting device database stored in a cloud environment module, wherein one or more lighting parameters of specific color temperatures are used to control a multi-spectrum light-emitting device; constructing a light scene database formed by multi-spectrum combinations based on user emotional needs; selecting and controlling light scenes; recording effective scenes to form a spectrum recipe database; and enabling users to store and share spectrum recipes via the cloud.


