Sleep Dream Guidance Using Biofeedback Scenario Selection
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Solution Overview
Problem
Existing brain-machine interface technologies do not guide subjects in a sleep state to have specific dreams, despite advancements in measuring brain activity.
Innovation Solution
A guidance device and method that acquires biological information, stores scenarios created from image data, and selects scenarios based on autonomic nervous system activity to stimulate the brain during sleep, using image and tactile stimuli to induce desired dreams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brain activity measurement technology is advanced to monitor sleep states, then the ability to detect biological information is improved, but the ability to guide or control dream content is not achieved
Solution Approach 1:
The system continuously monitors the subject's biological information (brain waves, heart rate, respiration) during sleep and uses this feedback to dynamically select and adjust the scenario being played back. The selecting unit chooses scenarios based on real-time autonomic nervous system activity patterns, creating a closed-loop system where measurement precision directly enables controlled guidance of dream content.
Solution Approach 2:
Multiple scenarios are pre-created and stored in the memory unit before the subject enters sleep. These scenarios, composed of image data associated with each other, are prepared in advance so that when the subject enters a suitable sleep state, the appropriate pre-prepared scenario can be immediately selected and played back to guide the dream experience.
2Adaptability or versatility
If multiple scenarios are stored in memory for dream guidance, then the versatility of dream content is improved, but the complexity of the system increases
Solution Approach 1:
Instead of storing complete complex dream experiences, the system stores scenarios as sequences of image data that are associated with each other. These image-based representations are simpler copies or representations of potential dream content, which can be played back to the subject's brain during sleep to induce specific dream scenarios without requiring the full complexity of actual dream memory storage.
Solution Approach 2:
The selecting unit dynamically chooses which scenario to play back based on real-time analysis of the subject's autonomic nervous system activity. The system transitions from static scenario storage to dynamic scenario selection, where the complexity is managed by using simple selection logic based on physiological parameters rather than complex real-time dream generation.
3Measurement precision
If scenario selection is based on autonomic nervous system activity, then the precision of dream guidance is improved, but the difficulty of detecting and measuring the appropriate biological signals increases
Solution Approach 1:
The biological information acquiring unit measures multiple physiological parameters simultaneously (brain waves, heart rate, respiration rate) to comprehensively assess the subject's autonomic nervous system activity. By using multiple universal biological indicators rather than relying on a single difficult-to-measure signal, the system achieves precise scenario selection through multi-functional measurement capabilities.
Data Source
AI summary
A guidance device includes a biological information acquiring unit configured to acquire biological information of a subject, a memory configured to store therein multiple scenarios created using multiple pieces of image data associated to each other, and a selecting unit configured to select, from among the multiple scenarios stored in the memory, the scenario related to the biological information acquired by the biological information acquiring unit.