Brain-Computer Interface Fuzzy Control for Immersive Interaction
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Solution Overview
Problem
Existing brain-computer interface devices suffer from unsatisfactory interactivity and weak sense of immersion in user interactions.
Innovation Solution
A method and apparatus for controlling brain-computer interface devices using electroencephalogram data and image data, employing fuzzy control to extract multi-modal features, obtain fuzzy sets, and execute control instructions, thereby enhancing user interaction and immersion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional control methods are used for VR devices, then the device can operate, but the interactivity between user and device is unsatisfactory
Solution Approach 1:
The patent replaces traditional mechanical control interfaces (buttons, controllers) with a brain-computer interface that directly reads electroencephalogram signals from the user's brain. This substitution enables more natural and immersive interaction by translating neural activity directly into control commands, resolving the contradiction between ease of operation and device complexity.
Solution Approach 2:
The patent introduces an intermediary system that processes electroencephalogram data and translates it into meaningful control instructions. This intermediary layer (including feature extraction modules, fuzzy logic systems, and command generation modules) bridges the gap between raw brain signals and device control, enabling intuitive interaction while managing system complexity through modular architecture.
2Adaptability or versatility
If traditional control methods are used for VR devices, then the device can function, but the sense of immersion is weak
Solution Approach 1:
The patent replaces traditional external control mechanisms with direct brain-computer communication, allowing users to interact with the virtual environment through their natural thoughts. This creates a more immersive experience by eliminating the disconnect between user intent and system response, while the modular BCI architecture manages the inherent complexity.
Solution Approach 2:
The patent implements a feedback mechanism where electroencephalogram data is continuously monitored and processed to generate real-time control responses. This closed-loop system enhances immersion by providing immediate feedback between the user's neural activity and the virtual environment responses, creating a more natural and engaging interaction cycle.
3Ease of operation
If electroencephalogram data and image data are processed using fuzzy control, then interactivity improves, but processing complexity increases
Solution Approach 1:
The patent transforms raw electroencephalogram and image data into meaningful features through parameter extraction and transformation. By converting complex raw data into simplified feature representations (such as frequency domain features from EEG and visual features from images), the system achieves high interactivity while managing processing complexity through effective dimensionality reduction.
Solution Approach 2:
The patent introduces fuzzy logic as an intermediary processing layer that handles the complexity of combining and interpreting multiple data sources. The fuzzy inference system acts as a mediator that processes extracted features from both EEG and image data, generating control commands without requiring complex explicit rule sets, thus improving interactivity while containing processing complexity.
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AI summary
Provided are a method and apparatus for controlling a brain-computer interface device, and the brain-computer interface device. In the method, subsequent to obtaining, by the brain-computer interface device, electroencephalogram data of a user, a multi-modal feature is extracted from the electroencephalogram data and image data displayed by the brain-computer interface device. A fuzzy set corresponding to the multi-modal feature is obtained by the brain-computer interface device based on the multi-modal feature. Further, a control instruction corresponding to the multi-modal feature is obtained by the brain-computer interface device based on the fuzzy set corresponding to the multi-modal feature. The control instruction is executed by the brain-computer interface device, in such a manner that the brain-computer interface device can be controlled based on the electroencephalogram data of the user and the image data displayed by the brain-computer interface device.