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Open and closed-loop control systems01:17

Open and closed-loop control systems

Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
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Related Experiment Video

Updated: May 13, 2026

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

Assisted closed-loop optimization of SSVEP-BCI efficiency.

Jacobo Fernandez-Vargas1, Hanns U Pfaff, Francisco B Rodríguez

  • 1Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid Madrid, Spain.

Frontiers in Neural Circuits
|February 28, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an assisted closed-loop protocol for brain-computer interfaces (BCI) using steady state visually evoked potentials (SSVEP). The new method improves BCI efficiency by enabling shared control between user and machine, outperforming traditional paradigms.

Keywords:
BCI illiteracyBCI performance predictoractivity-dependent stimulationbrain-computer interfacebrain-machine interfaceindividual alpha frequencyresting state EEGresting state network

Related Experiment Videos

Last Updated: May 13, 2026

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Traditional brain-computer interfaces (BCI) relying on steady state visually evoked potentials (SSVEP) heavily depend on user learning from feedback.
  • This reliance can limit performance due to inter-individual variability in user state and traits.

Purpose of the Study:

  • To develop and evaluate a novel assisted closed-loop optimization protocol for SSVEP-based BCIs.
  • To enhance BCI efficiency by enabling shared control and information between the user and the machine.
  • To compensate for inter-individual variability in user performance.

Main Methods:

  • Implemented a closed-loop protocol involving online adaptation of BCI stimuli properties.
  • Utilized a closed-loop search for optimal SSVEP flicker frequencies.
  • Provided real-time feedback of SSVEP magnitudes to both user and machine for adaptive parameter control.

Main Results:

  • The proposed protocol demonstrated significantly improved performance compared to classic SSVEP-BCI control paradigms in a study with 18 subjects.
  • Evidence suggests the protocol effectively accounts for inter-individual variability, with baseline EEG measures predicting BCI performance.
  • Real-time efficiency measurements were used to adapt BCI control parameters dynamically.

Conclusions:

  • Assisted closed-loop protocols show promising potential for advancing BCI systems.
  • This approach can enhance BCI efficiency and robustness by incorporating adaptive optimization and shared control.
  • Potential future applications include diagnostic/therapeutic tools and new paradigms for basic neuroscience research.