Neural Engagement Measurement via EEG Signal Segmentation
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Solution Overview
Problem
Traditional methods for measuring consumer engagement in neuromarketing lack accuracy and temporal granularity, and newer neurological data methods also face similar issues, necessitating a more precise and reliable approach.
Innovation Solution
A method that involves presenting stimuli, capturing neural data, calculating neural similarity, and generating engagement measures over time, which can include cross-brain correlations and prediction based on demographic or biometric data, allowing for precise engagement measurement without requiring a specific engagement model and enabling identification of neural outliers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods such as subjective ratings or exposure time measurement are used, then the measurement process is simple, but accuracy and temporal granularity are poor
Solution Approach 1:
The patent replaces traditional mechanical/survey-based measurement systems with a neurological measurement system using EEG technology. This substitution enables objective, continuous, and temporally granular engagement measurement by detecting electrical activity in the brain, thereby resolving the contradiction between measurement accuracy and system complexity.
Solution Approach 2:
The patent changes the measurement parameter from behavioral indicators (subjective ratings, exposure time) to neurological indicators (EEG signals, neural activity patterns). This parameter transformation enables more accurate and granular engagement measurement while providing objective data that captures real-time consumer responses.
2Measurement precision
If neurological data methods are used, then objectivity is improved, but temporal granularity and accuracy still suffer from similar issues
Solution Approach 1:
The patent segments the continuous EEG signal into discrete time windows and analyzes neural patterns at multiple temporal scales. This segmentation approach enables precise temporal granularity by identifying specific neural responses to different stimuli segments while maintaining reliability through aggregation across multiple measurements and subjects.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring EEG signals and adjusting analysis parameters in real-time. The system provides feedback loops that validate measurement consistency across trials and subjects, thereby improving both temporal granularity and reliability through iterative refinement of the measurement process.
Data Source
AI summary
A method for measuring engagement includes presenting a set of stimuli to a set of subjects, capturing neural data from the subjects, calculating a set of neural similarities between the first set of subjects, and generating a measure of engagement from the set of neural similarities.