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

被引:5
作者
Mahrooz, Mohammad H. [1 ,4 ]
Fattahzadeh, Farrokh [2 ]
Gharibzadeh, Shahriar [3 ]
机构
[1] Shahid Beheshti Med Univ, Tehran, Iran
[2] Univ Tehran Med Sci, Tehran, Iran
[3] Shahid Beheshti Univ, Inst Cognit & Brain Sci, Tehran, Iran
[4] Sharif Univ Technol, Dept Aerosp Engn, Tehran, Iran
关键词
Brain-computer interface; Neurofeedback; Biofeedback; Systems engineering; Functional flow block diagram; BIOFEEDBACK;
D O I
10.1007/s10484-023-09601-6
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
Brain-Computer Interface (BCI) and Neurofeedback (NF) both rely on the technology to capture brain activity. However, the literature lacks a clear distinction between the two, with some scholars categorizing NF as a special case of BCI while others view BCI as a natural extension of NF, or classify them as fundamentally different entities. This ambiguity hinders the flow of information and expertise among scholars and can cause confusion. To address this issue, we conducted a study comparing BCI and NF from two perspectives: the background and context within which BCI and NF developed, and their system design. We utilized Functional Flow Block Diagram (FFBD) as a system modelling approach to visualize inputs, functions, and outputs to compare BCI and NF at a conceptual level. Our analysis revealed that while NF is a subset of the biofeedback method that requires data from the brain to be extracted and processed, the device performing these tasks is a BCI system by definition. Therefore, we conclude that NF should be considered a specific application of BCI technology. By clarifying the relationship between BCI and NF, we hope to facilitate better communication and collaboration among scholars in these fields.
引用
收藏
页码:47 / 53
页数:7
相关论文
共 33 条
[1]   Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction [J].
Alimardani, Maryam ;
Hiraki, Kazuo .
FRONTIERS IN ROBOTICS AND AI, 2020, 7
[2]   Can We Predict Who Will Respond to Neurofeedback? A Review of the Inefficacy Problem and Existing Predictors for Successful EEG Neurofeedback Learning [J].
Alkoby, O. ;
Abu-Rmileh, A. ;
Shriki, O. ;
Todder, D. .
NEUROSCIENCE, 2018, 378 :155-164
[3]   Opening up the Design Space of Neurofeedback Brain-Computer Interfaces for Children [J].
Antle, Alissa N. ;
Chesick, Leslie ;
McLaren, Elgin-Skye .
ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION, 2018, 24 (06)
[4]   Electroencephalogram in humans [J].
Berger, H .
ARCHIV FUR PSYCHIATRIE UND NERVENKRANKHEITEN, 1929, 87 :527-570
[5]  
Buede D.M., 2008, ENG DESIGN SYSTEMS M, VSecond
[6]   Prefrontal Asymmetry BCI Neurofeedback Datasets [J].
Charles, Fred ;
De Castro Martins, Caio ;
Cavazza, Marc .
FRONTIERS IN NEUROSCIENCE, 2020, 14
[7]   Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications [J].
Douibi, Khalida ;
Le Bars, Solene ;
Lemontey, Alice ;
Nag, Lipsa ;
Balp, Rodrigo ;
Breda, Gabriele .
FRONTIERS IN HUMAN NEUROSCIENCE, 2021, 15
[8]   A psychoengineering paradigm for the neurocognitive mechanisms of biofeedback and neurofeedback [J].
Gaume, A. ;
Vialattea, A. ;
Mora-Sanchez, A. ;
Ramdani, C. ;
Vialatte, F. B. .
NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS, 2016, 68 :891-910
[9]  
Hammond D.C., 2011, J. Neurother., V15, P305, DOI [DOI 10.1080/10874208.2011.623090, 10.1080/10874208.2011.623090]
[10]   Brain-computer interfaces for EEG neurofeedback: Peculiarities and solutions [J].
Huster, Rene J. ;
Mokom, Zacharais N. ;
Enriquez-Geppert, Stefanie ;
Herrmann, Christoph S. .
INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY, 2014, 91 (01) :36-45