BCI Device Recovering Object Information via Brainwave Analysis
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Solution Overview
Problem
Current EEG and BCI technologies are limited to single-function applications and lack a method to recover object initial information into object expectation information by analyzing brainwave signals, thereby missing opportunities for mental training and multi-functional applications.
Innovation Solution
A device and method that analyze brainwave signals to determine mental activity classification or intensity, and then recover object initial information into object expectation information, achieving mental training and multi-functional application effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If EEG and BCI technologies are used for single-function applications, then the device complexity is reduced, but the adaptability and versatility are limited
Solution Approach 1:
The patent implements multi-functionality by integrating multiple application modules (mental training, gaming, productivity) within a single BCI system. The system can switch between different functions based on user selection, allowing one device to perform multiple tasks rather than requiring separate devices for each application type.
Solution Approach 2:
The system dynamically adjusts its operation mode and processing parameters based on real-time brainwave analysis. The control unit can switch between different processing algorithms and application modes depending on the detected mental state, making the system adaptable to varying user needs and conditions.
2Reliability
If brainwave signals are analyzed to recover object initial information into object expectation information, then the mental training effect is improved, but the processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary classification of brainwave signals into distinct mental states (focused attention, relaxed attention, creative state, flow state) before proceeding to the information recovery process. This preliminary categorization speeds up subsequent processing by allowing the system to select appropriate recovery algorithms based on the detected state rather than performing exhaustive analysis.
Solution Approach 2:
The system creates simplified representations or models of the original information based on brainwave patterns. Instead of directly recovering complex information, it generates simplified copies or proxies that capture the essential characteristics, reducing computational burden while maintaining training effectiveness.
3Measurement precision
If multiple brainwave signals are analyzed simultaneously for multiple mental activities, then the classification accuracy is improved, but the device complexity and processing load increase
Solution Approach 1:
The patent segments the analysis process into distinct stages: signal acquisition, preliminary classification into mental states, and then specific information recovery based on the detected state. This segmentation allows the system to handle multiple signals by processing them through standardized stages rather than requiring complex simultaneous analysis of all signals at once.
Solution Approach 2:
The control unit acts as an intermediary that receives multiple brainwave signals, classifies them into mental states, and then directs them to appropriate processing modules. This intermediary layer simplifies the overall system architecture by providing a standardized interface between signal acquisition and application-specific processing.
Data Source
AI summary
A device, used in conjunction with a brainwave detector, includes a display unit configured to display at least one object initial information of an application program, a brainwave signal receiving module for receiving at least one brainwave signal from the brainwave detector, an analysis and judgment unit analyzing and judging the at least one brainwave signal to generate mental activity classification information, and a recover control unit generating alteration information based on the mental activity classification information, recovering the object initial information into object expectation information according to the alteration information, and displaying the object expectation information on the display unit.