Audio Stimulus Optimization for Brain State Control
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Solution Overview
Problem
Current methods fail to provide personalized and effective stimuli to achieve desired brain states, as sound perception is largely subjective and existing technologies lack the ability to optimize brain states in a personalized manner.
Innovation Solution
A system and method that decodes human brain activity to predict brain state values and identifies informative stimulus features, generating optimal audio or other stimuli to achieve specific brain states, such as focus or enjoyment, through the use of brain activity signals and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personalized brain state optimization is implemented using decoded brain activity and machine learning models, then the effectiveness and personalization of stimuli are improved, but the system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex task of brain state optimization into distinct components: brain activity signal acquisition, signal preprocessing, feature extraction, machine learning model training, and stimulus generation. Each component is handled by specialized modules, making the overall system more manageable and implementable despite its complexity
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw brain activity signals and stimulus generation. These models serve as mediators that translate complex neural data into actionable insights for personalized stimulus optimization, bridging the gap between neuroscience and practical application
2Adaptability or versatility
If brain activity signals are decoded and machine learning models are trained to predict brain state values, then personalized brain state optimization is achieved, but the measurement and detection difficulty increases
Solution Approach 1:
The patent replaces direct mechanical or physical measurement of brain states with computational approaches. Machine learning models substitute for traditional measurement methods, using pattern recognition and data analysis to infer brain state values from neural signals, thereby reducing the difficulty of direct measurement
Solution Approach 2:
The system transforms brain activity signals from their raw form into extracted features that are more suitable for analysis. By changing the parameters from raw voltage/time signals to meaningful features (such as frequency components, amplitude characteristics), the system makes the data more accessible and easier to work with for personalization
Data Source
AI summary
A method and system for generating an optimal audio stimulus for achieving a target brain state value for a brain state. The method and system can be used to generate one or more brain state models which can decode brain activity signals to predict brain state values. The brain state models can be applied to brain activity signals captured while users are performing tasks with an audio stimulus. Audio features of the audio stimulus can be extracted. An audio-brain model can be trained on the predicted brain state values and the audio features. From the trained audio-brain model, the optimal audio stimulus can be generated.