Emulated Brain Signals for Invasive BCI Decoder Development
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Solution Overview
Problem
Developing invasive brain computer interface (iBCI) decoders is hindered by the difficulty in recruiting participants willing to undergo invasive electrode implantation, limiting the availability of brain data for decoder development.
Innovation Solution
A non-invasive system using a motion capture device and artificial neural network (ANN) to generate emulated brain signals from human operators' kinematic data, allowing for the development and testing of iBCI decoders without invasive procedures, enabling faster and more cost-effective development with multiple participants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive electrode implantation is used to acquire brain data, then measurement precision of brain signals is improved, but ease of operation and participant recruitment deteriorates
Solution Approach 1:
The patent creates a computational copy of the brain's neural processing function using an artificial neural network. This ANN copy is trained on real brain data from invasive recordings and then used to generate synthetic brain signals from non-invasive kinematic inputs, effectively copying the complex neural transformation process without requiring physical brain access.
Solution Approach 2:
The patent introduces an intermediary computational system (the artificial neural network) that mediates between non-invasive kinematic measurements and the desired brain signal outputs. This intermediary translates body movement data into emulated neural activity patterns, bridging the gap between accessible external measurements and inaccessible internal brain states.
2Reliability
If invasive electrode implantation is used to acquire brain data, then reliability of brain data is improved, but device complexity and ethical constraints worsen
Solution Approach 1:
The patent replaces the mechanical/invasive electrode implantation system with a non-invasive computational system. Instead of physically inserting electrodes into the brain to measure neural activity, the system uses motion capture technology combined with trained artificial neural networks to generate reliable brain signal data through mathematical modeling and data transformation.
Solution Approach 2:
The patent changes the fundamental parameters of data acquisition from direct neural measurement (requiring invasive procedures) to kinematic measurement combined with computational transformation. This parameter change shifts the system from measuring electrical activity directly at the neural level to inferring neural activity patterns from movement data through trained models.
3Manufacturing precision
If more brain data is collected from invasive recordings to improve decoder performance, then manufacturing precision of decoder algorithms is improved, but loss of time and recruitment difficulty worsen
Solution Approach 1:
The patent performs preliminary action by pre-training the artificial neural network on extensive invasive brain data recordings collected during the development phase. This pre-trained model then serves as a reusable component that can generate synthetic brain data for multiple decoder development scenarios without requiring additional invasive procedures, saving time in subsequent development iterations.
Solution Approach 2:
The patent creates a universal data generation system where a single trained artificial neural network can generate brain signals for multiple different decoders, experimental conditions, and participant populations. This universal model replaces the need to conduct separate invasive recording sessions for each decoder development effort, enabling parallel development of multiple algorithms.
Data Source
AI summary
Systems, methods, and protocols for developing invasive brain computer interface (iBCI) decoders non-invasively by using emulated brain data are provided. A human operator can interact in real-time with control algorithms designed for iBCI. An operator can provide input to one or more computer models (e.g., via body gestures), and this process can generate emulated brain signals that would otherwise require invasive brain electrodes to obtain.