EEG Neural Activity Decoding with Environmental Stimulus Data
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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 machine learning model is trained using EEG data and concurrent environmental data to decode neural activity, incorporating visual, audio, and language encoders to infer user intentions and control computer operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional input methods (controllers, speech recognition, eye tracking) are used in VR/AR devices, then user input capability is provided, but device complexity increases and user effort increases
Solution Approach 1:
The patent extracts the input function from physical hardware (controllers, sensors) and relocates it to the brain itself through EEG monitoring. By taking out the need for external input devices and using neural activity directly as the input source, the system reduces device complexity while improving ease of operation.
Solution Approach 2:
The system enables the user's brain to serve as the input device itself. Through EEG sensors detecting neural activity and machine learning models interpreting brain signals, the user's own neural patterns become the control mechanism, eliminating the need for separate controllers or sensors and reducing overall device complexity.
2Measurement precision
If EEG data alone is used to decode neural activity, then measurement is simplified, but accuracy deteriorates due to vast variability of brain responses
Solution Approach 1:
The patent merges multiple data sources (EEG data, environmental stimulus data, and user feedback) into a unified machine learning model. This combination allows the system to compensate for the variability of individual brain responses by correlating neural activity with known stimuli and observed outcomes, thereby improving measurement precision without requiring overly complex individual sensors.
Solution Approach 2:
The system incorporates feedback loops where the decoded neural activity and resulting system responses are fed back to the user, allowing the machine learning model to continuously refine its accuracy. This feedback mechanism enables the system to adapt to individual brain variability over time, improving precision while managing complexity through iterative learning rather than complex hardware.
3Adaptability or versatility
If multiple input techniques are added to VR/AR devices, then input versatility is improved, but ease of operation deteriorates due to increased user effort and complexity
Solution Approach 1:
The patent makes the brain itself a universal input device that can perform multiple functions (selection, navigation, authentication, control) through different patterns of neural activity. By training the machine learning model to recognize various neural patterns, a single EEG-based system replaces multiple specialized input devices, improving versatility while reducing the effort required to switch between input methods.
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.