Vehicle Brain-Machine Interface for Non-Invasive Infotainment Control
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Solution Overview
Problem
Current brain-machine interface (BMI) systems for automotive vehicle control lack integration with infotainment systems, particularly in semi-autonomous vehicles, limiting user interaction and control granularity.
Innovation Solution
A BMI system that uses electric field encephalography (EFEG) to read brain electrical impulses, processing them to control vehicle infotainment systems without invasive electrodes, allowing for real-time command execution and integrating with vehicle computing systems to streamline user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If non-invasive EFEG sensors are used to measure brain electrical activity, then user comfort and safety are improved, but signal measurement precision deteriorates compared to invasive electrodes
Solution Approach 1:
The patent uses non-invasive EFEG sensors as an intermediary to detect brain electrical activity without direct contact with brain tissue. These sensors measure electrical fields through the scalp and skull, serving as a mediator between the brain's neural activity and the control system, thereby avoiding the harmful effects of invasive electrodes while maintaining functional capability
Solution Approach 2:
The patent replaces the mechanical invasive electrode system with a non-invasive electrical field measurement system. Instead of physically penetrating or contacting brain tissue with electrodes, the system uses EFEG sensors to detect electrical fields through the skull, substituting a mechanical contact-based approach with a field-based detection method
2Adaptability or versatility
If BMI control is integrated with infotainment systems, then user interaction capability is improved, but system complexity increases
Solution Approach 1:
The patent merges the BMI control system with the vehicle's existing infotainment system, combining neural signal processing capabilities with multimedia and vehicle control functions. This integration allows a single unified system to handle both entertainment functions and driver intent recognition, reducing the need for separate dedicated hardware systems
Solution Approach 2:
The infotainment system is enhanced to serve multiple functions: traditional multimedia playback, navigation, vehicle settings control, and now BMI-based driver intent recognition and control. This multi-functionality allows the same hardware platform to support diverse applications without requiring separate specialized systems
3Speed
If real-time neural data processing is implemented, then control responsiveness is improved, but computational energy consumption increases
Solution Approach 1:
The system performs preliminary processing of neural signals by extracting relevant features and filtering out noise before full analysis. Pre-trained models and predefined gesture patterns allow the system to quickly match incoming neural data against known commands, reducing the computational burden during real-time operation while maintaining responsive control
Solution Approach 2:
The BMI system implements self-calibration and adaptive learning capabilities that allow it to optimize its own performance over time. The system automatically adjusts to individual user neural patterns and improves signal processing efficiency without requiring external intervention, thereby reducing ongoing computational energy requirements
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
Enhances user control over vehicle infotainment systems with increased granularity, reducing manual operation and minimizing driver distraction, while enabling seamless interaction with semi-autonomous vehicle functions.
Implementation Method 1
wireless receivers utilize sensors to measure electrical activity of the brain to determine actual as well as potential electrical field activity using functional MRI (fMRI), electroencephalography (EEG) or electric field encephalography (EFEG) receivers
Data Source
AI summary
Embodiments describe a vehicle configured with a brain machine interface (BMI) for a vehicle computing system to control vehicle functions using electrical impulses from motor cortex activity in a user's brain. A BMI training system trains the BMI device to interpret neural data generated by a motor cortex of a user and correlate the neural data to a vehicle control command associated with a neural gesture emulation function. A BMI system onboard the vehicle may receive a neural data feed of neural data from the user using the trained BMI device, determine, a user intention for a control instruction to control a vehicle infotainment system using the neural data feed, and perform an action based on the control instruction. The vehicle may further include a headrest configured as a Human Machine Interface (HMI) device that reads the electrical impulses without invasive electrode connectivity.