BMI Gesture Hierarchy for Accurate Group and Element Selection
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Solution Overview
Problem
Current brain-machine interfaces (BMIs) struggle with accurately and intuitively selecting elements and groups, particularly when multiple selections are made consecutively, leading to slow and imprecise interactions for individuals with motor impairments.
Innovation Solution
A system and method utilizing a neural decoding system to identify neural signals associated with intended gestures, allowing for the visualization of groups and elements, and enabling precise selection through primary and secondary gestures, such as wrist movements and digit presses, to facilitate high-accuracy mental control of devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional BMI methods are used for element selection, then the system is simple to operate, but the selection accuracy and speed are low
Solution Approach 1:
The system segments the selection process into two distinct phases: group selection (using primary gestures) and element selection (using secondary gestures). This segmentation allows the complex task of selecting elements from grouped structures to be broken down into manageable steps, improving overall selection accuracy while maintaining operational simplicity through clear phase separation.
Solution Approach 2:
The patent introduces a hierarchical dimension to the selection process by organizing elements into groups and subgroups. This dimensional organization transforms the traditional flat selection interface into a multi-level structure, enabling more precise element selection through primary and secondary gesture differentiation without proportionally increasing system complexity.
2Productivity
If traditional BMI methods are used for element selection, then the system is easy to operate, but the interaction speed is slow
Solution Approach 1:
The system performs preliminary grouping of elements before the actual selection process. By pre-organizing elements into logical groups and subgroups, the system reduces the search space for selection, thereby increasing interaction speed. The primary gesture selects the group beforehand, and the secondary gesture then selects the specific element, making the overall process faster while remaining easy to operate.
3Measurement precision
If traditional BMI methods are used for element selection, then the interface is simple, but the selection precision for grouped elements is low
Solution Approach 1:
The interface is segmented into multiple selection levels corresponding to different hierarchical levels of elements. Primary gestures operate at the group level while secondary gestures operate at the element level within the selected group. This segmentation enables precise selection of nested elements without requiring a completely complex interface, as each level has its own dedicated control mechanism.
Solution Approach 2:
The interface dynamically adapts its behavior based on the selection state. When a group is selected via primary gesture, the system transitions to a state where secondary gestures can select elements within that group. This dynamic behavior allows the interface to remain simple at any given moment while providing precise selection capabilities across multiple levels when needed.
Data Source
AI summary
High accuracy selections of grouped elements can be achieved using a brain machine interface. A neural decoding system can: receive a neural signal from a neural recording device, identify that the neural signal is representative of the user at least thinking of attempting to perform a gesture, which is one of a plurality of known gestures mapped to a plurality of commands, and output a command based on the gesture (the gesture is mapped to the command). A controller can be in communication with the neural decoding system and a display. The controller can: receive the command, if the gesture is a primary gesture, then select a group to be an active group of elements, if the gesture is a secondary gesture, then at least select an element of the active group of elements and output a response indicative of the group being activated and/or the element being selected.