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Related Experiment Video

Updated: May 12, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Intra-day signal instabilities affect decoding performance in an intracortical neural interface system.

János A Perge1, Mark L Homer, Wasim Q Malik

  • 1School of Engineering, Brown University, Providence, RI, USA. janos_perge@brown.edu

Journal of Neural Engineering
|April 12, 2013
PubMed
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Neural interface systems (NIS) for motor control show significant daily signal variability, primarily due to physiological changes rather than artifacts. Adapting decoding methods to these instabilities is key for improving prosthetic control in individuals with paralysis.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Motor neural interface systems (NIS) translate neural signals into prosthetic or assistive device control for individuals with paralysis.
  • Signal variability in NIS can degrade prosthetic control, necessitating characterization of biological and technological sources of instability.

Purpose of the Study:

  • To analyze the frequency and causes of neural signal variability in spike-based NIS.
  • To assess the impact of signal instability on motor prosthetic control performance.

Main Methods:

  • Analysis of within-day fluctuations in spiking activity and action potential amplitude from silicon microelectrode arrays.
  • Recordings were made in the motor cortex of three individuals with tetraplegia during the BrainGate pilot clinical trial.

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Tools for Surface Treatment of Silicon Planar Intracortical Microelectrodes
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Tools for Surface Treatment of Silicon Planar Intracortical Microelectrodes

Published on: June 8, 2022

Related Experiment Videos

Last Updated: May 12, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Tools for Surface Treatment of Silicon Planar Intracortical Microelectrodes
06:39

Tools for Surface Treatment of Silicon Planar Intracortical Microelectrodes

Published on: June 8, 2022

  • Computer simulations were used to model the effect of neuronal rate changes on decoded neural cursor movements.

    • Main Results:

    • 84% of recorded units showed significant changes in firing rate, and 74% showed changes in spike amplitude within a single session.
    • 40% of sessions indicated potential array micro-movement due to correlated amplitude changes across electrodes.
    • 85% of rate changes stemmed from physiological mechanisms, not artifacts, leading to directional bias in 56% of cursor control assessments.

    Conclusions:

    • Signal instability, particularly physiological rate changes, significantly impacts NIS performance.
    • Developing adaptive signal acquisition and decoding methods is crucial for enhancing intracortically-based NIS.
    • Future advancements in NIS will rely on robust algorithms that accommodate neural signal variability.