EEG-Facial Response Estimation System for Market Research
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Solution Overview
Problem
Current market research methods are time-consuming and expensive, and existing AI technologies struggle to predict users' responses to visual stimuli effectively, especially when targeting a large audience, as they require presenting the stimulus to a limited audience for analysis.
Innovation Solution
A system and method that combines facial expression analysis with EEG signals to derive emotional and cognitive features, creating a training dataset to estimate and classify user responses to stimuli, allowing for the prediction of responses to new stimuli based on correlated features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional market research methods are used to capture and measure user responses to visual stimuli, then measurement precision can be achieved, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces traditional mechanical survey methods (pen and paper surveys, one-on-one interviews, focus group discussions) with an automated computer vision system that captures facial expressions and EEG signals to measure user responses to visual stimuli in real-time, eliminating the time-consuming nature of traditional research methods while maintaining measurement precision
Solution Approach 2:
The patent introduces facial expression analysis and EEG signal processing as intermediary mechanisms between the user and the research system. These intermediaries automatically capture and quantify user responses to visual stimuli, providing objective measurement data without requiring direct user input or lengthy interview processes
2Productivity
If facial expression analysis is used to predict user responses, then productivity improves, but reliability decreases due to limited audience sampling
Solution Approach 1:
The patent combines multiple data sources (facial expression data, EEG signals, and demographic information) to create a composite prediction model. This multi-component approach enhances prediction reliability by cross-validating results across different measurement modalities while maintaining high productivity through automated processing
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously refines its prediction algorithms based on accumulated data from multiple users. The training dataset is updated with new facial expression and EEG data, allowing the system to improve prediction accuracy over time while maintaining rapid processing speeds
3Measurement precision
If stimulus presentation to limited audience is conducted for response analysis, then measurement precision is maintained, but productivity decreases
Solution Approach 1:
The patent creates a universal measurement system that can handle multiple users simultaneously through automated facial expression and EEG analysis. The system processes visual stimuli response data from numerous users in parallel, maintaining precise measurement capabilities while dramatically increasing research throughput compared to traditional one-on-one interview methods
Data Source
AI summary
A method for training a system for measuring or estimating or both of a user's response to a stimulus and for classifying the response is disclosed. The system is trained using a test stimulus presented to one or more users. One or more images of the users' face are captured and simultaneously EEG signals of the users are captured. One or more emotional features are derived from the facial data of the users. One or more cognitive features and emotional features are derived from the EEG signals of each of the users, and a training dataset is created by correlating the one or more emotional features from the facial data, one or more cognitive and emotional features from the EEG signals with one or more features associated with the test stimulus. Created training dataset is used for measuring or estimating or both of a user's response to the stimulus.