Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Rigid control of motor unit firing rates in the human tibialis anterior muscle persists during neurofeedback.

Journal of neurophysiology·2026

Same author

Peripheral neural interfaces for reading high-frequency brain signals.

Nature biomedical engineering·2025

Same author

Corticomuscular coherence during upright standing in unilateral transfemoral amputees.

Brain communications·2025

Same author

Control limitations in the null-space of the wrist muscle system.

Scientific reports·2024

Same author

Discrimination of finger movements by magnetomyography with optically pumped magnetometers.

Scientific reports·2023

Same author

Mental Tasks Modulate Motor-Units Above 10 Hz and are a Potential Control Signal for Movement Augmentation: a Preliminary Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023

Same journal

Synaptic micromechanics and brain softening as a mechanobiological hypothesis for Alzheimer's disease.

Frontiers in neuroscience·2026

Same journal

The relationship between healthy sleep patterns and the risk of scoliosis: a large prospective cohort study.

Frontiers in neuroscience·2026

Same journal

Dynamic functional reorganization in post-stroke aphasia: a state-of-the-art fMRI review from disease evolution to intervention.

Frontiers in neuroscience·2026

Same journal

Correction: Case Report: A possible novel adult-onset, progressive MAO-A hypofunction.

Frontiers in neuroscience·2026

Same journal

Respiratory modulation of neurophysiology and symptoms in athletes with sports-related concussion: a randomized crossover trial.

Frontiers in neuroscience·2026

Same journal

Impact of C-reactive protein-triglyceride-glucose and systemic immune-inflammation indices on obstructive sleep apnea in older adults with depression.

Frontiers in neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 16, 2026

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

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

Published on: July 26, 2013

Unsupervised adaptation of brain-machine interface decoders.

Tayfun Gürel¹, Carsten Mehring

¹Bernstein Center Freiburg, Albert-Ludwig University of Freiburg Freiburg, Germany ; Department of Bioengineering, Imperial College London London, UK.

Frontiers in Neuroscience

|November 20, 2012

Summary

This summary is machine-generated.

This study introduces an unsupervised method for brain-machine interfaces (BMI) to continuously adapt neural decoders. This approach maintains high performance without interrupting user operation or requiring explicit movement intention data.

Keywords:

brain-computer interface brain-machine interfaces movement decoding optimal feedback control unsupervised learning

More Related Videos

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

Related Experiment Videos

Last Updated: May 16, 2026

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

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

Published on: July 26, 2013

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Neural decoder performance in brain-machine interfaces (BMI) degrades over time due to non-stationarities.
Periodic calibration phases, while effective, disrupt autonomous BMI operation and lead to performance instability between calibrations.

Purpose of the Study:

To develop an efficient, unsupervised method for continuous adaptation of BMI decoders.
To enable BMI systems to maintain high performance during autonomous operation without explicit user intention data.

Main Methods:

Proposed an unsupervised training method utilizing a cost function derived from neuronal recordings.
Employed a learning algorithm guided by the cost function to evaluate and adapt decoding parameters.
Verified the method through simulations using an optimal feedback control model of a BMI user.

Main Results:

The adaptive BMI decoder generated fast and precise trajectories in simulations.
The unsupervised method effectively handled initially unknown and non-stationary tuning parameters.
Long-term performance stability was achieved even with changing neural parameters.

Conclusions:

The presented unsupervised adaptation method offers a superior alternative to periodic calibration for BMI systems.
This approach ensures continuous, stable, and high performance of neural decoders in BMI applications.
The algorithm shows promise for enhancing the autonomy and usability of BMI technology.