Brain-Computer Interface Using Latent Navigation Beyond 2D Control
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Solution Overview
Problem
Conventional brain-computer interface (BCI) technologies primarily focus on 2-dimensional control, which limits the high-dimensional actuation capabilities of users, and fail to leverage the brain's natural plasticity for advanced control tasks.
Innovation Solution
A method and system that enables users to navigate a high-dimensional latent space of a generative model using neural signals, allowing control of abstract neural cursors to bypass the compressive bottleneck of biological actuators, utilizing a brain-computer interface to translate neural states into semantic outputs like images or text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional 2-dimensional control methods are used in BCI, then the system is simple to operate, but the actuation capability is limited and cannot leverage high-dimensional neural information
Solution Approach 1:
The patent transitions from 2-dimensional control to high-dimensional control by mapping neural states to latent variables in a high-dimensional latent space of a generative model. This allows the BCI system to utilize the full richness of neural information, enabling control of complex outputs such as images, text, and other semantic data with fine-grained precision.
Solution Approach 2:
The patent introduces a generative model as an intermediary between the neural state and the output control. The neural state is transformed into latent variables that serve as control signals for the generative model, which then produces the desired output. This intermediary structure enables high-dimensional actuation while maintaining system manageability.
2Measurement precision
If high-dimensional latent space navigation is implemented, then neural information resolution is preserved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary transformation of neural states into latent variables using a pre-trained generative model. By pre-computing the mapping between neural states and latent space coordinates, the system preserves high-dimensional neural information resolution while reducing the computational burden during real-time control operations.
Solution Approach 2:
The patent changes the parameter space from raw neural signals to latent variables that capture the essential structure of neural information. This parameter transformation maintains the high resolution of neural data while organizing it in a computationally manageable form that can be efficiently navigated for control purposes.
3Measurement precision
If the brain's natural plasticity is leveraged for advanced control, then control precision improves, but the training and adaptation process becomes more complex
Solution Approach 1:
The patent implements a feedback mechanism where the user receives information about their neural state's projection onto the latent space manifold. This feedback allows users to learn and adapt their neural control strategies, leveraging the brain's plasticity to achieve precise control of high-dimensional outputs through iterative practice and reinforcement.
Data Source
AI summary
The method can include: recording a neural state, determining a latent array, and determining an output based on the latent array. In variants, the method can function to enable a subject to use neural signals to navigate within a high-dimensional concept space to control an output (e.g., image generation, motor control, communication, etc.).