Adaptive multimodal neural stimulation coordination method and wearable device

By combining multiple stimulation modes and machine learning algorithms, a real-time adaptive neural stimulation system is realized, solving the problems of unstable stimulation effects and low energy efficiency in the existing technology, and significantly improving the efficiency and user experience of neural information transmission.

CN119733169APending Publication Date: 2025-04-01COSONIC INTELLIGENT TECH CO LTD
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Patent Information

Application Number
CN202411550462.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-01
Publication Date
2025-04-01

AI Technical Summary

Technical Problem

Existing neural stimulation technologies are difficult to adapt to complex and changeable physiological states and environmental conditions, and lack real-time adaptability, resulting in unstable stimulation effects, poor user experience and low energy utilization efficiency.

Method used

By integrating various stimulation modes such as touch, hearing, vision and temperature, combined with machine learning algorithms and context perception technology, highly personalized and intelligent neural stimulation is achieved. Specific steps include obtaining multimodal sensor data, generating current status characterization, selecting initial stimulation mode, generating collaborative neural stimulation, collecting neural feedback signals, evaluating stimulation effects, and updating stimulation mode parameters through reinforcement learning algorithms.

Benefits of technology

It significantly improves the efficiency and accuracy of neural information transmission, improves stimulation effect and user experience, improves energy efficiency, and maintains excellent stability during long-term use.

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Abstract

The present invention belongs to the technical field of neural engineering and biomedical engineering, and more specifically, to an adaptive multimodal neural stimulation collaborative method and a wearable device, which obtains multimodal sensor data, including user physiological data and environmental data; generates a current state representation through a context perception module based on the multimodal sensor data; selects an initial stimulation mode from a preset stimulation mode library according to the current state representation; generates collaborative neural stimulation through a multimodal stimulation generator based on the initial stimulation mode; collects the user's neural feedback signal for collaborative neural stimulation; evaluates the stimulation effect through a neural signal decoding module based on the neural feedback signal; updates the stimulation mode parameters using a reinforcement learning algorithm; adjusts the output of the multimodal stimulation generator; and significantly improves the efficiency and accuracy of neural information transmission. Experimental results show that the stimulation effect of the present invention is nearly 80% higher than that of a single modality system.
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