Brainwave Database Evolution for Real-Time Neurofeedback
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Solution Overview
Problem
Existing systems for detecting brainwaves and providing feedback are limited by the need for retrospective analysis, delayed interpretation, and lack of real-time feedback, making it difficult for individuals to immediately understand and adjust their physiological signals during sleep or neurofeedback training.
Innovation Solution
An automatic evolution method and system that uses a brainwave database to classify and analyze physiological information, establish a feedback algorithm model based on neural networks, and provide real-time feedback through a remote terminal, allowing immediate adjustment of brainwave signals using visual or auditory feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If physiological signals are collected and uploaded to cloud platform for retrospective analysis, then data can be stored and analyzed, but real-time feedback cannot be provided to the subject
Solution Approach 1:
The system segments the analysis process by deploying lightweight analysis models to edge devices (smart beds, portable devices) for immediate local processing, while only uploading necessary data to the cloud platform. This segmentation enables real-time feedback without requiring complete cloud-based processing, thus reducing feedback delay while maintaining analytical capability.
Solution Approach 2:
The patent introduces an intermediary layer (edge computing devices, local processing units) between the signal collection and cloud platform. This intermediary performs preliminary analysis and generates real-time feedback locally, while synchronizing with the cloud platform for comprehensive analysis, thereby solving the contradiction between real-time feedback and cloud-based retrospective analysis.
2Measurement precision
If functional magnetic resonance imaging is used for real-time neurofeedback, then detailed brain imaging can be obtained, but the equipment is expensive and the process takes more than 30 minutes
Solution Approach 1:
The patent replaces expensive, complex functional MRI equipment with lower-cost, portable brainwave detection devices (electrode pads, smart beds with sensors). While these devices have different technical characteristics, they provide sufficient accuracy for neurofeedback applications at a fraction of the cost and with much faster response times, making the technology accessible for home use.
Solution Approach 2:
The system changes the detection parameters from fMRI's detailed spatial imaging to EEG-style temporal dynamics detection. By focusing on temporal patterns of brainwave activity rather than detailed spatial maps, the system achieves effective neurofeedback with simpler, faster, and more affordable equipment suitable for home environments.
3Loss of information
If existing smart bed system collects physiological signals during sleep, then sleep data can be monitored, but immediate feedback cannot be transmitted to the subject
Solution Approach 1:
The system performs preliminary analysis of physiological signals locally on the smart bed or connected portable device as the data is being collected. This preliminary processing identifies key patterns and generates immediate feedback recommendations before the data is uploaded to the cloud, ensuring that subjects receive real-time feedback during or immediately after sleep events without waiting for cloud processing.
Data Source
AI summary
An automatic evolution method used for a brainwave database which collects physiological information of brainwaves about healthy and clinical groups, the automatic evolution method includes: classifying the physiological information of brainwaves collected by the brainwave database according to data characteristics; establishing a feedback algorithm model based on a neural network architecture according to the physiological information of brainwaves classified by the parameters; using the feedback algorithm model to input a subject's physiological information of brainwaves; measuring an accuracy of the subsequent performance data calculated by the feedback algorithm model; and incorporating the physiological information of brainwaves of the subject into the brainwave database, establishing an updated feedback algorithm model based on an updated neural network architecture, and feeding a comparison result generated by the updated feedback algorithm model back to the subject.