EEG Neural Decoding with Environmental Data for Intuitive VR/AR Input
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Solution Overview
Problem
Existing VR/AR devices lack efficient and intuitive methods for user input, particularly in constrained environments like head-mounted computers, and face challenges in accurately interpreting neural activity due to the vast variability of brain responses to stimuli.
Innovation Solution
A multi-modal neural decoding system using EEG data combined with environmental and behavioral data, trained through machine learning models, to infer user intentions and control computer operations, such as menu selections, through neural activity analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional input methods (keyboard, touchscreen, controllers) are used in VR/AR devices, then basic input functionality is provided, but the devices become more complex and require additional hardware components
Solution Approach 1:
The patent replaces mechanical input devices (controllers, keyboards, touchscreens) with a neural decoding system that directly interprets brain activity through EEG sensors. This substitution eliminates the need for complex external input hardware while providing more intuitive control, as users can interact with VR/AR environments through their thoughts rather than physical actions.
Solution Approach 2:
The system utilizes the user's own neural activity as the input source, requiring no external controllers or interfaces. The EEG sensors mounted on the head-mounted device directly capture brain signals, and the neural decoding model translates these signals into control commands, making the user's brain the sole input mechanism.
2Adaptability or versatility
If multiple input techniques (speech recognition, eye tracking, gesture recognition) are implemented, then input versatility is improved, but the amount of hardware and processing requirements increase
Solution Approach 1:
The neural decoding model is designed to handle multiple types of tasks and commands through a single unified system. Rather than implementing separate hardware systems for speech, eye tracking, and gesture recognition, the patent uses one neural decoding framework that can interpret various neural patterns corresponding to different intended actions, providing versatile input control through a single mechanism.
Solution Approach 2:
The patent combines multiple input functionality into a single neural decoding system. Instead of having separate eye tracking sensors, gesture cameras, and speech microphones, the system merges these functions by using EEG sensors to capture neural activity that reflects the user's intent across different interaction modalities, reducing hardware complexity while maintaining versatility.
3Measurement precision
If EEG sensors are used to detect neural activity, then contact with the scalp is required for accurate measurement, but this reduces user comfort and wearability
Solution Approach 1:
The patent employs flexible EEG sensor arrays that can conform to the contours of the scalp, distributing contact points across a larger area. These thin, flexible sensors maintain good electrical contact for accurate neural activity detection while being comfortable enough for extended wear during VR/AR sessions.
Solution Approach 2:
The EEG sensing system is divided into multiple discrete sensor contacts distributed across the head rather than requiring a single large contact area. This segmentation allows the sensors to capture neural activity from multiple locations simultaneously while minimizing pressure and discomfort at each individual contact point, improving both measurement precision and user comfort.
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 interaction by allowing intuitive control of VR/AR devices using neural activity, improving accuracy and reducing the need for additional hardware, thus providing a more natural and efficient input method.
Implementation Method 1
Neurons in the underlying brain tissue generate electrical activity in the form of ionic currents that can be measured as voltage differences in the electrodes
Data Source
AI summary
In an embodiment, a computer-implemented method for decoding neural activity is provided. In the method, at least one machine learning model is trained using a training data set of EEG data and concurrently collected environmental data collected from data collection participants. Once the at least one machine learning model is trained, EEG data measured from sensors attached to or near a user's head is received. Environmental data describing stimulus the user is exposed to concurrently with the measurement of the EEG data is also received. The EEG data and the environmental data is input into the at least one machine learning model to determine an inference related to the neural activity. Based on the inference, an operation of a computer program is altered.