Sleep Sound Stimulation Using Neural EEG Response Prediction
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Solution Overview
Problem
Existing sound stimulation methods for sleep regulation rely on external similarities between sleep EEG and sound stimuli, leading to inefficient and suboptimal selection of sound stimuli, and lack personalized optimization.
Innovation Solution
A deep neural network-based system selects sound stimuli that maximize sleep-related neural activities by internal correlations, using a closed-loop optimization process to personalize sound selection without requiring extensive subject testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sound stimuli are selected based on external similarities between sleep EEG and sound stimuli, then the selection process is simple, but the effectiveness of sleep regulation is insufficient
Solution Approach 1:
The patent introduces a deep neural network as an intermediary between sound stimuli and sleep EEG. Instead of directly comparing external features, the neural network learns internal correlations from training data, acting as a mediator that maps sound characteristics to their effects on sleep-related neural activities, thereby resolving the contradiction between simple selection and effective regulation
Solution Approach 2:
The patent transforms the sound stimulus selection from based on external acoustic features to based on predicted internal neural activity parameters. By changing the selection criterion from acoustic feature similarity to predicted EEG response parameters, the system achieves both automated selection and improved effectiveness
2Reliability
If extensive subject testing is performed to optimize sound selection, then personalized sleep regulation is achieved, but the time and resource consumption increase significantly
Solution Approach 1:
The patent performs preliminary action by training the deep neural network on extensive sound-EEG data before actual use. The neural network learns optimal sound-sleep relationships in advance, allowing the system to make personalized sound selections for new users without requiring extensive individual testing, thus reducing time consumption while maintaining personalization
Solution Approach 2:
The patent creates a virtual copy of the neural processing system through the trained deep neural network. This digital model replicates and predicts neural responses to sound stimuli, allowing the system to optimize sound selection for individuals by copying and adapting from population-level learning rather than requiring extensive individual empirical testing
Data Source
AI summary
The present invention describes a system and method for selecting and optimizing a sound stimulus using a deep neural network to regulate and improve human sleep quality. The deep neural network has the capability of characterizing processing of human brain cortical neurons for external stimulus (images, sounds, etc.) information. By inputting massive sound stimuli into the deep neural network, a sound mode which causes model-estimated sleep electroencephalograph to be optimal can be found, the sound mode is applied to a real human body, and the intensity of corresponding sleep waves of the human body in different sleep stages is enhanced through closed-loop optimization so as to realize the purpose of regulating sleep. The present invention mainly aims at solving the technical problem of how to select and optimize, when a sound stimulus means (music, speech, natural sounds, white/colored noise, etc.) is used to assist in human sleep.