Vehicle Brain-Machine Interface With Fuzzy EEG Control States
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Solution Overview
Problem
Existing brain-machine interface (BMI) systems for vehicle control, such as those described in Korean Patent Application Publication No. KR101632830, lack the reliability to buffer unintended control commands, particularly when using electric field encephalography (EEG) data for automotive signal control.
Innovation Solution
The implementation of a BMI system that utilizes fuzzy state logic with Gaussian kernel-type membership functions to smoothly transition between vehicle control states, such as speed and turn angles, by decoding continuous neural data feeds from the motor cortex, allowing for robust and granular user control through a trained correlation model integrated into an autonomous vehicle controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If EEG data is used for vehicle control commands, then the system enables brain-machine interface control, but the reliability is insufficient to buffer unintended control commands
Solution Approach 1:
The patent introduces fuzzy state logic as an intermediary layer between EEG signal acquisition and vehicle control command execution. This mediator processes raw EEG data through Gaussian kernel-type membership functions, transforming discrete control bits into smooth transitional states that buffer unintended commands while maintaining system reliability without excessive complexity
Solution Approach 2:
The system implements beforehand cushioning by using fuzzy state logic to anticipate and buffer unintended control commands before they reach the vehicle control system. The Gaussian kernel-type membership functions create transitional states that soften abrupt EEG-triggered commands, preventing harmful effects from unreliable raw signals
2Ease of operation
If discrete control bits are used from EEG, then the control system is simple, but smooth transitions between vehicle control states cannot be achieved
Solution Approach 1:
The patent applies parameter changes by transforming discrete control bits into continuous fuzzy states using Gaussian kernel-type membership functions. This changes the parameter representation from binary/discrete to continuous/probabilistic, enabling smooth transitions between vehicle control states such as acceleration, deceleration, and steering angles
Solution Approach 2:
The system implements dynamics by making the control states adaptable and transitional rather than fixed and discrete. The fuzzy state logic dynamically adjusts control output based on the degree of membership in different states, allowing smooth, gradual transitions between vehicle control commands rather than abrupt switches
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
This approach enhances the reliability and convenience of BMI control systems by providing smooth transitions between vehicle control states, preventing inadvertent commands and enabling users with limited physical capabilities to exercise precise control over vehicles, while maintaining robustness and autonomy.
Implementation Method 1
non-invasive techniques where wireless receivers measure brain potential fields using electric field encephalography (EEG) receivers
Data Source
AI summary
Embodiments describe a system configured with a brain machine interface (BMI) system implemented in a vehicle for performing vehicle functions using electrical impulses from motor cortex activity in a user's brain. The system uses fuzzy states for increased robustness. The fuzzy states are defined by sets of Gaussian kernel-type membership functions that are defined for steering and velocity action function states. The membership functions define fuzzy states that provide overlapping control tiers for increasing and decreasing vehicle functionality. An autonomous vehicle may perform control and governance of transitions between membership functions that may overlap, resulting in smooth transitioning between the states.