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Physiological properties of brain-machine interface input signals.

Marc W Slutzky^1,2,3, Robert D Flint⁴

¹Department of Neurology, Northwestern University, Chicago, Illinois; mslutzky@northwestern.edu.

Journal of Neurophysiology

|June 16, 2017

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Brain-machine interfaces (BMIs) decode neural signals for device control. This review examines invasive BMI signal types—intracortical spikes, electrocorticography, and epidural signals—to improve clinical viability and performance.

Area of Science:

Neuroscience
Biomedical Engineering
Rehabilitation Technology

Background:

Brain-machine interfaces (BMIs) offer potential for controlling external devices via neural signals.
Despite experimental success, high-performance, clinically viable BMIs for widespread use remain undeveloped.
Signal selection is a critical factor influencing BMI performance and clinical applicability.

Purpose of the Study:

To review and compare the physiological characteristics and performance of various invasive BMI input signals.
To assess signal properties such as movement-related information, longevity, and stability.
To discuss future strategies for enhancing BMI input signal performance.

Main Methods:

Review of existing literature on invasive BMI signal types.

Keywords:

ECoG LFP brain-machine interface epidural signals longevity spikes stability

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Analysis of physiological characteristics including movement-related information, longevity, and stability.

Discussion of hardware improvements and signal processing for enhanced decoding.

Main Results:

Intracortical spikes, electrocorticography (ECoG), and epidural signals are key invasive BMI signal types.
Each signal type exhibits distinct characteristics in terms of information content, longevity, and stability.
Performance is influenced by signal acquisition methods and decoding algorithms.

Conclusions:

Optimizing the selection and processing of neural signals is crucial for advancing BMI technology.
Future enhancements require improvements in hardware and a deeper understanding of neural signal physiology.
Enhanced BMIs hold promise for improved clinical applications and patient outcomes.