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Related Experiment Video

Updated: May 25, 2026

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
07:05

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Published on: August 24, 2017

Single-trial classification of feedback potentials within neurofeedback training with an EEG brain-computer

Eduardo López-Larraz1, Iñaki Iterate, Carlos Escolano

  • 1Instituto de Investigación en Ingeniera de Aragónand, and Dpto de Informática e Ingeniería de Sistemas, Universidad de Zaragoza, Spain. edulop@unizar.es

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
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This study shows that Brain-Computer Interfaces (BCI) can effectively decode neurofeedback stimuli during training. This technology can measure subject adherence and improve cognitive performance enhancement therapies.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Cognitive Science

Background:

  • Neurofeedback therapy is an emerging technique for treating neuropsychological disorders and enhancing cognitive functions.
  • Feedback stimuli are crucial for guiding brain rhythm learning during neurofeedback.
  • Online decoding of feedback stimuli can assess subject compliance and adherence.

Purpose of the Study:

  • To model and classify performance feedback potentials using a Brain-Computer Interface (BCI).
  • To evaluate the effectiveness of BCI in real-time neurofeedback training.
  • To compare classification techniques for decoding feedback stimuli.

Main Methods:

  • Utilized a Brain-Computer Interface (BCI) for real-time data acquisition during neurofeedback.

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Related Experiment Videos

Last Updated: May 25, 2026

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
07:05

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Published on: August 24, 2017

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
06:11

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Published on: April 18, 2025

  • Modeled and classified performance feedback potentials from five human subjects.
  • Employed Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) classification algorithms.

    • Main Results:

    • Both LDA and SVM classification techniques achieved an average performance of approximately 80%.
    • Demonstrated the feasibility of online decoding of neurofeedback stimuli.
    • Indicated successful measurement of subject adherence during training.

    Conclusions:

    • BCI technology is effective for online decoding of neurofeedback stimuli.
    • LDA and SVM are viable methods for classifying performance feedback potentials with high accuracy.
    • This approach can enhance the assessment of subject engagement in neurofeedback therapies.