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Related Experiment Video

Updated: May 8, 2026

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Brain-computer interfaces for EEG neurofeedback: peculiarities and solutions.

René J Huster1, Zacharais N Mokom2, Stefanie Enriquez-Geppert3

  • 1Experimental Psychology Lab, Department of Psychology, European Medical School, Carl von Ossietzky University, Oldenburg, Germany; Research Center Neurosensory Science, Carl von Ossietzky University Oldenburg, Oldenburg, Germany.

International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology
|September 10, 2013
PubMed
Summary

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This summary is machine-generated.

Neurofeedback training, an early brain-computer interface (BCI) application, uses electroencephalogram (EEG) data. This study reviews EEG-based BCIs and introduces a new software solution for multi-subject trials.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science

Background:

  • Neurofeedback training, a brain-computer interface (BCI) application, has existed for decades.
  • Most neurofeedback research utilizes electroencephalogram (EEG) recordings for clinical applications, particularly for psychopathological syndromes.
  • The clinical focus influences neurofeedback BCI technology, often relying on minimally processed EEG features.

Purpose of the Study:

  • To highlight the unique characteristics of EEG-based neurofeedback BCIs.
  • To discuss the implications of these characteristics for software development.
  • To introduce a novel software solution tailored for neurofeedback BCI research and clinical trials.

Main Methods:

  • Review of existing brain-computer interface software packages.
Keywords:
BCIEEGNeurofeedbackSoftware

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  • Analysis of the peculiarities of electroencephalogram (EEG)-based neurofeedback BCIs.
  • Development and presentation of a new software solution with specific features for multi-subject handling.

    • Main Results:

    • EEG-based neurofeedback BCIs have distinct features compared to other BCI applications.
    • Existing software packages may not adequately address the needs of multi-subject neurofeedback studies.
    • The proposed software solution incorporates features like pseudo-/sham-neurofeedback for enhanced experimental and clinical trial design.

    Conclusions:

    • Understanding the specificities of EEG-based neurofeedback BCIs is crucial for effective software implementation.
    • There is a need for specialized software that supports multi-subject handling in neurofeedback research.
    • The introduced software offers practical solutions for conducting robust experimental and clinical trials in neurofeedback.