Decoding the Debate: A Comparative Study of Brain-Computer Interface and Neurofeedback

Mahrooz, Mohammad H.; Fattahzadeh, F.; Gharibzadeh, Shahriar

doi:10.1007/s10484-023-09601-6

Cited by 5 publications

(9 citation statements)

References 30 publications

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“…Unlike offline processing, where the fully captured time series is typically analyzed after data recording is complete, real-time processing analyzes each new incoming data point immediately [105,116]. Accordingly, real-time processing is critical in systems such as brain-computer interfaces and neurofeedback implementations because both interact with real-time brain signals [117]. However, real-time processing presents some challenges, including the fact that the full data set is not available in advance, making some fNIRS-specific (pre)processing algorithms infeasible.…”

Section: (B) Increasing Signal Qualitymentioning

confidence: 99%

From Lab to Life: Challenges and Perspectives of fNIRS for Hemodynamic-based Neurofeedback in Real-World Environments

Klein,

Kohl,

Lührs

et al. 2023

Preprint

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Neurofeedback allows individuals to monitor and self-regulate their brain activity, potentially improving human brain function. Beyond the traditional electrophysiological approach using primarily electroencephalography (EEG), brain hemodynamics measured with functional magnetic resonance imaging (fMRI) and more recently functional near-infrared spectroscopy (fNIRS) have been used, particularly to improve the spatial specificity of neurofeedback. Over the last years, especially fNIRS has attracted great attention because it offers several advantages over fMRI such as increased user accessibility, cost-effectiveness, and mobility - the latter being the most distinct feature of fNIRS. The next logical step would be to transfer hemodynamic neurofeedback protocols that have already been proven and validated by fMRI to mobile fNIRS. However, this undertaking is not always easy, especially since fNIRS novices may miss important fNIRS-specific methodological challenges. This review is aimed at researchers from different fields who wish exploiting the unique capabilities of fNIRS for neurofeedback. It carefully addresses fNIRS-specific challenges and offers suggestions for possible solutions. If the challenges raised are addressed and further developed, fNIRS could emerge as a useful neurofeedback technique with its own unique application potential - the targeted training of brain activity in real-world environments, thereby significantly expanding the scope and scalability of hemodynamic neurofeedback applications.

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Section: (B) Increasing Signal Qualitymentioning

confidence: 99%

From Lab to Life: Challenges and Perspectives of fNIRS for Hemodynamic-based Neurofeedback in Real-World Environments

Klein,

Kohl,

Lührs

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…This allows for immediate feedback and control. BCIs and NFB rely on brain imaging data obtained using techniques such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), or functional near-infrared spectroscopy (fNIRS), and their real-time data processing includes data streaming, (pre-)processing, and feedback generation (Naseer and Hong, 2015 ; Enriquez-Geppert et al, 2017 ; Sitaram et al, 2017 ; Mahrooz et al, 2023 ). Despite certain methodological similarities, there are different definitions of BCI and NFB depending on the source or expert, particularly regarding their areas of application (Mahrooz et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

“…BCIs and NFB rely on brain imaging data obtained using techniques such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), or functional near-infrared spectroscopy (fNIRS), and their real-time data processing includes data streaming, (pre-)processing, and feedback generation (Naseer and Hong, 2015 ; Enriquez-Geppert et al, 2017 ; Sitaram et al, 2017 ; Mahrooz et al, 2023 ). Despite certain methodological similarities, there are different definitions of BCI and NFB depending on the source or expert, particularly regarding their areas of application (Mahrooz et al, 2023 ). BCIs are often described as systems that enable direct communication between the brain and external devices, allowing individuals to interact with their environment using their own brain signals (Naseer and Hong, 2015 ; Paulmurugan et al, 2021 ; Saha et al, 2021 ; Mahrooz et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

“…Despite certain methodological similarities, there are different definitions of BCI and NFB depending on the source or expert, particularly regarding their areas of application (Mahrooz et al, 2023 ). BCIs are often described as systems that enable direct communication between the brain and external devices, allowing individuals to interact with their environment using their own brain signals (Naseer and Hong, 2015 ; Paulmurugan et al, 2021 ; Saha et al, 2021 ; Mahrooz et al, 2023 ). BCIs are used in different fields such as assistive technology, neuroprosthetics, and entertainment (Millán, 2010 ; Rupp et al, 2014 ; Abdulkader et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

“…Because of the methodological overlap, BCI and NFB probably cannot be considered as completely independent methods. Since both applications involve some kind of feedback mechanism, NFB could be viewed as a specific form of BCI method that focuses on (therapeutic) training and self-regulation of brain activity (Mahrooz et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

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Optimizing spatial specificity and signal quality in fNIRS: an overview of potential challenges and possible options for improving the reliability of real-time applications

Klein

2024

Front. Neuroergonomics

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The optical brain imaging method functional near-infrared spectroscopy (fNIRS) is a promising tool for real-time applications such as neurofeedback and brain-computer interfaces. Its combination of spatial specificity and mobility makes it particularly attractive for clinical use, both at the bedside and in patients' homes. Despite these advantages, optimizing fNIRS for real-time use requires careful attention to two key aspects: ensuring good spatial specificity and maintaining high signal quality. While fNIRS detects superficial cortical brain regions, consistently and reliably targeting specific regions of interest can be challenging, particularly in studies that require repeated measurements. Variations in cap placement coupled with limited anatomical information may further reduce this accuracy. Furthermore, it is important to maintain good signal quality in real-time contexts to ensure that they reflect the true underlying brain activity. However, fNIRS signals are susceptible to contamination by cerebral and extracerebral systemic noise as well as motion artifacts. Insufficient real-time preprocessing can therefore cause the system to run on noise instead of brain activity. The aim of this review article is to help advance the progress of fNIRS-based real-time applications. It highlights the potential challenges in improving spatial specificity and signal quality, discusses possible options to overcome these challenges, and addresses further considerations relevant to real-time applications. By addressing these topics, the article aims to help improve the planning and execution of future real-time studies, thereby increasing their reliability and repeatability.

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Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study

Wang,

Zhang,

Han

et al. 2024

CURR MED SCI

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Decoding the Debate: A Comparative Study of Brain-Computer Interface and Neurofeedback

Cited by 5 publications

References 30 publications

From Lab to Life: Challenges and Perspectives of fNIRS for Hemodynamic-based Neurofeedback in Real-World Environments

From Lab to Life: Challenges and Perspectives of fNIRS for Hemodynamic-based Neurofeedback in Real-World Environments

Optimizing spatial specificity and signal quality in fNIRS: an overview of potential challenges and possible options for improving the reliability of real-time applications

Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study

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