Eye-Tracking Interface Using Neural Intent Signals for XR Target Selection
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Solution Overview
Problem
Existing human-computer interface systems struggle to accurately and intuitively select targets in extended reality environments, particularly due to the limitations of gaze-based interactions and the need for additional confirmation gestures, which can be cumbersome and error-prone.
Innovation Solution
A system and method utilizing an oculomotor-brain-computer interface that combines eye-tracking and neural signal analysis to detect anticipatory negative potentials, such as Stimulus-Preceding Negativity (SPN) and Contingent Negative Variation (CNV), to determine the user's intent to select a target, providing real-time feedback and enabling precise target selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If gaze-based interaction is used for target selection, then the interface is simple and intuitive, but the selection accuracy is low and false positives occur
Solution Approach 1:
The patent combines gaze-based interaction with neural signal-based interaction to create a hybrid system. The eye-tracking unit captures gaze patterns while the neural signal capture unit detects brain electrical activity, and the processing unit integrates both signals to make target selection decisions, thereby improving accuracy while maintaining ease of use
Solution Approach 2:
The neural signal capture unit acts as an intermediary that provides additional confirmation between gaze fixation and target selection. The processing unit uses neural signals as a mediator to verify user intent, filtering out false positives by requiring both gaze fixation and appropriate neural response
2Measurement precision
If additional confirmation gestures are required for target selection, then the selection accuracy improves, but the operation complexity increases and user experience deteriorates
Solution Approach 1:
The system uses the user's own neural signals as the confirmation mechanism, eliminating the need for external confirmation gestures. The brain's natural neural response to anticipated stimuli serves as the confirmation signal, making the system self-verifying without requiring additional user actions
Solution Approach 2:
The patent replaces mechanical confirmation gestures with neural signal-based confirmation. Instead of requiring physical actions like clicking or pressing buttons, the system detects neural signals (such as stimulus-preceding negativity and contingent negative variation) that naturally occur when users intend to select a target
3Measurement precision
If neural signal analysis is added to eye-tracking, then the target selection accuracy improves, but the device complexity increases
Solution Approach 1:
The processing unit is designed to handle multiple functions: it processes both eye-tracking data and neural signal data, integrates the two signal types, and makes the final selection decision. This multi-functional approach consolidates complexity into a single processing component rather than requiring separate systems for each function
Data Source
AI summary
A human-computer interface system and method for target selection on a computer-generated display includes an eye-tracking unit configured to capture gaze patterns of a user, a neural signal capture unit configured to detect neural signals (such as electroencephalogram (EEG) signals) from the user, and a processing unit. The processing unit is configured to analyze the gaze patterns and neural signals to identify an anticipatory negative potential associated with the user's intent to select a particular target, and to initiate a target selection based on the identified anticipatory negative potential.