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

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

State Space to Transfer Function

State Space to Transfer Function

The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:

Transfer Function to State Space

Transfer Function to State Space

State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

You might also read

Related Articles

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

Sort by

Same author

Data dependent peak model based spectrum deconvolution for analysis of high resolution LC-MS data.

Analytical chemistry·2014

Same author

Demonstration of a large-scale optical exceptional point structure.

Optics express·2014

Same author

[The effect of RABEX-5 downregulation on the chemosensitivity of human breast cancer cells].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2014

Same author

Optimization of spatial filter with volume Bragg gratings in photo-thermo-refractive glass.

Optics letters·2014

Same author

Age-related white matter degradation rule of normal human brain: the evidence from diffusion tensor magnetic resonance imaging.

Chinese medical journal·2014

Same author

Prenatal and postnatal polycyclic aromatic hydrocarbon exposure, airway hyperreactivity, and Beta-2 adrenergic receptor function in sensitized mouse offspring.

Journal of toxicology·2014

Search research articles

Related Experiment Video

Updated: Dec 6, 2025

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

Covariant Cluster Transfer for Kernel Reinforcement Learning in Brain-Machine Interface.

Xiang Zhang, Yiwen Wang

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

|October 6, 2020

Summary

This study introduces a novel covariant cluster transfer mechanism to improve Brain-Machine Interface (BMI) decoders. The new method enhances adaptation across sessions, benefiting users with motor impairments.

More Related Videos

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

Related Experiment Videos

Last Updated: Dec 6, 2025

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

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Brain-Machine Interfaces (BMIs) restore motor functions for disabled individuals using neural signals.
Decoder performance degrades due to neural signal variations from fatigue or distraction.
Current recalibration requires professional data labeling, hindering home use.

Purpose of the Study:

To develop an adaptive mechanism for Brain-Machine Interface (BMI) decoders to accelerate cross-session adaptation.
To enable patients to easily set reward signals for decoder parameter adjustment.
To leverage past session data for faster learning in new sessions.

Main Methods:

Proposed a covariant cluster transfer mechanism integrated with kernel reinforcement learning (RL).
Clustered neural patterns from previous sessions to represent conditional distributions.
Transferred nearest clusters for distinct neural patterns in new sessions to utilize prior knowledge.

Main Results:

The algorithm was tested on simulated neural data with varying session distributions.
The covariant cluster transfer mechanism showed significantly higher success rates compared to random initialization and weight transfer.
Faster adaptation was observed when the conditional distribution between neural signals and actions remained similar.

Conclusions:

The proposed covariant cluster transfer mechanism effectively speeds up decoder adaptation in Brain-Machine Interfaces (BMIs).
This approach reduces the need for manual recalibration, improving user convenience for home-based applications.
The method shows promise for enhancing the robustness and usability of BMI systems.