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Assessment and Communication for People with Disorders of Consciousness

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Brain-machine interface control using broadband spectral power from local field potentials.

Siddharth Dangi, Kelvin So, Amy L Orsborn

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|October 11, 2013

Summary

This summary is machine-generated.

Closed-loop decoder adaptation (CLDA) rapidly improved brain-machine interface (BMI) control using local field potentials (LFPs). This method enabled sustained high performance in non-human primates for cursor tasks.

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Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Brain-machine interfaces (BMIs) show progress in control and task complexity.
Closed-loop decoder adaptation (CLDA) enhances and maintains BMI system performance.

Purpose of the Study:

Demonstrate the first use of CLDA for high-performance control of LFP-based BMIs.
Assess rapid performance improvement and long-term maintenance with CLDA.

Main Methods:

Trained a non-human primate to control a 2-D cursor using LFP activity.
Applied CLDA to adaptively update the decoder during a center-out reaching task.

Main Results:

Achieved rapid, high-performance cursor control with an average success rate of 74% ± 7%.
Demonstrated sustained high performance over 4 days with minimal decoder adaptations.
Observed rapid convergence in decoder parameters.

Conclusions:

CLDA effectively facilitates LFP-based BMI systems.
CLDA enables both rapid performance enhancement and long-term maintenance in BMIs.