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Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
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...
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ITACA: An open-source framework for Neurofeedback based on Brain-Computer Interfaces.

Diego Marcos-Martínez1, Eduardo Santamaría-Vázquez1, Víctor Martínez-Cagigal1

  • 1Biomedical Engineering Group (GIB), E.T.S Ingenieros de Telecomunicación, University of Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain.

Computers in Biology and Medicine
|May 18, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces ITACA, an open-source neurofeedback (NF) framework with novel metrics and gamified scenarios. ITACA enhances NF research by offering a flexible platform for designing and evaluating brain-computer interface training paradigms.

Keywords:
Brain–computer interfacesCognition researchElectroencephalographyNeurofeedbackNeurotechnology

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science

Background:

  • Neurofeedback (NF) offers a non-pharmacological approach for brain disorders.
  • Current NF frameworks lack diverse metrics and engaging training scenarios.
  • Limitations hinder user performance and research advancement in NF.

Purpose of the Study:

  • Introduce ITACA, a novel open-source framework for NF.
  • Overcome limitations of existing NF frameworks.
  • Facilitate the design, implementation, and evaluation of NF training paradigms.

Main Methods:

  • Developed ITACA with a user-friendly, flexible, and engaging interface.
  • Integrated three gamified training scenarios with five real-time brain activity metrics, including novel functional connectivity and network theory metrics.
  • Validated the framework through computational efficiency analysis and an NF protocol for frontal-medial theta modulation.

Main Results:

  • Computational efficiency analysis confirmed optimal feedback update rates for all implemented metrics.
  • The NF training protocol demonstrated ITACA's utility in NF research.
  • Results support the framework's capability to support advanced NF studies.

Conclusions:

  • ITACA provides a versatile platform for NF research.
  • The framework's features enable researchers to expand the state-of-the-art in NF.
  • ITACA supports the design, execution, and evaluation of sophisticated NF training paradigms.