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Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model
Francis R Willett1,2,3,4, Daniel R Young5,6, Brian A Murphy5,6
1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA. fwillett@stanford.edu.
Scientific Reports
|June 22, 2019
Summary
Simulating brain-computer interface performance can improve decoder design. A new feedback control model accurately predicts performance, enhancing speed and efficiency for users with intracortical brain-computer interfaces (iBCIs).
Area of Science:
- Neuroscience
- Biomedical Engineering
- Rehabilitation Engineering
Background:
- Intracortical brain-computer interfaces (iBCIs) use decoders to translate neural signals into intended movements.
- Offline-optimized decoders often underperform in real-time closed-loop operation due to unmodeled user and task interactions.
- Current calibration methods do not account for emergent dynamics between the decoder, user, and task parameters like target size.
Purpose of the Study:
- To investigate the feasibility of simulating online iBCI performance for improved decoder optimization.
- To develop a user-specific feedback control model to predict performance variations.
- To guide decoder parameter selection and design for enhanced real-time functionality.
Main Methods:
- Three participants in the BrainGate2 pilot clinical trial used a linear velocity decoder to control a computer cursor.
- Decoder parameters (gain, temporal smoothing) and task parameters (target radius, distance) were systematically varied.
- A user-specific iBCI feedback control model was developed and validated on held-out data.
Main Results:
- The developed model accurately predicted changes in user performance across different decoder and task parameters.
- Optimization of a nonlinear speed scaling function using the model improved the dynamic range of decoded speeds.
- Online implementation of the optimized decoder reduced target acquisition time compared to a standard optimized decoder.
Conclusions:
- Simulating iBCI performance is a feasible approach for quantitative decoder optimization and design.
- User-specific feedback control models can effectively predict and improve real-time iBCI performance.
- This approach holds promise for enhancing the usability and effectiveness of intracortical brain-computer interfaces.
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