Brain Signal Robot Control via Flashing Icon Detection
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Solution Overview
Problem
Current brain-machine interface systems are limited in their ability to effectively control robots using brain electrical signals for tasks such as feeding, watching videos, notification, and communication, particularly for individuals with paralysis or elderly users, as they require complex setups and lack intuitive interaction methods.
Innovation Solution
A system comprising a host computer, brain electrical signal detection device, manipulator, and screen that uses flashing icons to detect and process brain signals, allowing users to select functions like feeding, watching videos, or calling by comparing detected signals with personal reference parameters to execute corresponding actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brain-machine interface systems use complex setups to detect and control robots, then measurement precision of brain signals can be improved, but device complexity increases and ease of operation decreases
Solution Approach 1:
The system segments the brain signal processing into distinct functional modules: signal acquisition module, signal processing module, feature extraction module, and robot control module. Each module handles specific tasks independently, reducing overall system complexity while maintaining detection precision through specialized processing at each stage.
Solution Approach 2:
The patent introduces an intermediary processing layer between brain signal detection and robot control. This intermediary includes signal filtering, feature extraction, and pattern recognition components that translate raw brain signals into actionable control commands, simplifying the interface between user intent and robot execution.
2Measurement precision
If brain-machine interface systems use complex setups for signal detection, then measurement precision improves, but ease of operation deteriorates
Solution Approach 1:
The system incorporates adaptive learning algorithms that automatically adjust to individual user characteristics and signal patterns. The system performs self-calibration and adapts to user-specific brain wave patterns without requiring manual configuration, making operation intuitive and simple while maintaining high detection precision through personalized parameter optimization.
Solution Approach 2:
The patent dynamically adjusts processing parameters such as filter frequencies, threshold values, and sampling rates based on real-time signal quality and user state. This adaptive parameter adjustment maintains optimal detection precision across different usage conditions while requiring minimal user intervention or configuration.
3Adaptability or versatility
If the system provides multiple functions for robot control, then adaptability improves, but device complexity increases
Solution Approach 1:
The patent implements a universal control interface that can execute multiple robot control functions through a single integrated system architecture. The same brain signal processing pipeline supports various control tasks including robot navigation, object manipulation, and interaction commands, achieving multi-functionality without proportionally increasing system complexity through shared hardware and software resources.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables users to control a robot system through brain signals for various functions, enhancing the usability and accessibility for paralyzed or elderly individuals by simplifying the interaction process and improving the intuitive control of robotic tasks.
Implementation Method 1
detecting a brain electrical signal generated by the user when the user stares at one first level icon
Data Source
AI summary
A system for controlling a robot by brain electrical signal, includes a screen, an electronic signal detection device, and a host computer. The screen shows a plurality of icons thereon, and the plurality of icons flashes at different frequencies. The electrical signal detection device detects brain electrical signal when one of the plurality of icons is stared. The host computer stores a plurality of personal reference parameters corresponding to the plurality of icons of the screen. The host computer processes the brain electrical signal to get parameters of use, and compares the parameters of use with the plurality of personal reference parameters to choose and execute the icon which is stared.