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These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

Updated: May 11, 2026

Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke

Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke

Published on: September 1, 2023

Neuromuscular electrical stimulation induced brain patterns to decode motor imagery.

C Vidaurre¹, J Pascual, A Ramos-Murguialday

¹Machine Learning Group, Berlin Institute of Technology, Berlin, Germany. carmen.vidaurre@tu-berlin.de

Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology

|May 7, 2013

Summary

Neuromuscular electrical stimulation (NMES) patterns can improve brain-computer interface (BCI) efficiency by decoding motor imagery. This method offers a new way to train BCI systems for users with and without motor control difficulties.

Keywords:

Afferent patterns BCI-inefficency Efferent pattern classification Motor imagery Neuromuscular electrical stimulation

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Last Updated: May 11, 2026

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Non-Invasive Electrical Brain Stimulation Montages for Modulation of Human Motor Function

Non-Invasive Electrical Brain Stimulation Montages for Modulation of Human Motor Function

Published on: February 4, 2016

Area of Science:

Neuroscience
Biomedical Engineering
Rehabilitation Technology

Background:

Brain-computer interfaces (BCIs) are crucial for restoring function but often suffer from inefficiency, particularly in patient populations.
Existing solutions to BCI inefficiency have shown limited success.
Motor imagery (MI) is a common BCI control paradigm, but its effectiveness can be challenging for some users.

Purpose of the Study:

To investigate the potential of using afferent patterns induced by neuromuscular electrical stimulation (NMES) to improve BCI performance.
To explore whether NMES-induced afferent patterns can support the calibration and decoding of motor imagery (MI) in BCI systems.
To assess the feasibility of this approach for both healthy users and patients with motor impairments.

Main Methods:

Electroencephalography (EEG) data were recorded from 10 healthy participants during NMES of hands and feet and during MI of the same limbs.
Features and classifiers were extracted from the EEG data.
The ability to decode MI using classifiers trained on NMES-induced afferent patterns was evaluated.

Main Results:

Offline analysis demonstrated successful decoding of MI using a classifier trained on afferent patterns evoked by NMES.
The NMES-based classifier model was found to be superior to models trained solely on MI data.
This indicates that afferent patterns from NMES can effectively represent and decode motor intentions.

Conclusions:

Afferent patterns generated by NMES can effectively support BCI system calibration and facilitate the decoding of motor imagery.
This novel approach may offer a new method for training sensorimotor rhythm (SMR)-based BCIs, especially for healthy individuals struggling with BCI control.
It presents a promising alternative for training MI-based BCIs in individuals unable to perform voluntary movements but possessing residual afferent pathways, such as stroke or ALS patients.