TY - JOUR

T1 - Roadmap for Enhancing the Efficiency of Neurofeedback

AU - Bazanova, Olga M.

AU - Nikolenko, Ekaterina D.

AU - Zakharov, Alexander V.

AU - Barry, Robert J.

N1 - Bazanova, O. M., Nikolenko, E. D., Zakharov, A. V., & Barry, R. J. (2025). Roadmap for enhancing the efficiency of neurofeedback. NeuroRegulation, 12(2), 112–131.

PY - 2025

Y1 - 2025

N2 - This article presents a roadmap of ways to improve the effectiveness of EEG neurofeedback training (NFT) based on a literature review and our own research on internal and external factors affecting NFT outcomes. Here we provide a justification for the expediency of using individually determined EEG indices as a feedback signal, based on an analysis of the alpha peak frequency and the level of neuronal activation. As personalization of the NFT for self-regulation means receiving information from a unique neurophysiological parameter inherent only to this individual, the basic internal socioeconomic, psychological, and physiological factors play an important role in training efficiency. Also, external factors such as the delay and modality of feedback presentation, valence of reinforcement, electrode localization, visual condition, body position, duration, and number of NFT sessions, forehead muscle tension and EMG artifact contamination will be discussed. A rationale for each step of this roadmap will be given from the point of view of how this or that factor can influence the personalization and consequently, the effectiveness of self-regulation training with NFT. The article provides a forward-looking opportunity to optimize NFT, providing a sketch setting out the necessary steps.

AB - This article presents a roadmap of ways to improve the effectiveness of EEG neurofeedback training (NFT) based on a literature review and our own research on internal and external factors affecting NFT outcomes. Here we provide a justification for the expediency of using individually determined EEG indices as a feedback signal, based on an analysis of the alpha peak frequency and the level of neuronal activation. As personalization of the NFT for self-regulation means receiving information from a unique neurophysiological parameter inherent only to this individual, the basic internal socioeconomic, psychological, and physiological factors play an important role in training efficiency. Also, external factors such as the delay and modality of feedback presentation, valence of reinforcement, electrode localization, visual condition, body position, duration, and number of NFT sessions, forehead muscle tension and EMG artifact contamination will be discussed. A rationale for each step of this roadmap will be given from the point of view of how this or that factor can influence the personalization and consequently, the effectiveness of self-regulation training with NFT. The article provides a forward-looking opportunity to optimize NFT, providing a sketch setting out the necessary steps.

KW - electroencephalography

KW - feedback presentation

KW - individual alpha peak frequency

KW - neurofeedback technology

KW - neuronal activation

UR - https://www.scopus.com/pages/publications/105011865811

UR - https://www.mendeley.com/catalogue/7444952d-3863-3548-b184-4d12471b7e96/

U2 - 10.15540/nr.12.2.112

DO - 10.15540/nr.12.2.112

M3 - Article

VL - 12

SP - 112

EP - 131

JO - NeuroRegulation

JF - NeuroRegulation

SN - 2373-0587

IS - 2

ER -