EEG neurofeedback research: A fertile ground for psychiatry?

The clinical efficacy of neurofeedback is still a matter of debate. This paper analyzes the factors that should be taken into account in a transdisciplinary approach to evaluate the use of EEG NFB as a therapeutic tool in psychiatry. Neurofeedback is a neurocognitive therapy based on human-computer...

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Bibliographic Details
Published in:	Encéphale Vol. 45; no. 3; p. 245
Main Authors:	Batail, J-M, Bioulac, S, Cabestaing, F, Daudet, C, Drapier, D, Fouillen, M, Fovet, T, Hakoun, A, Jardri, R, Jeunet, C, Lotte, F, Maby, E, Mattout, J, Medani, T, Micoulaud-Franchi, J-A, Mladenovic, J, Perronet, L, Pillette, L, Ros, T, Vialatte, F
Format:	Journal Article
Language:	English
Published:	France 01-06-2019
Subjects:	Cognitive Behavioral Therapy - methods Electroencephalography Humans Mental Disorders - therapy Neurofeedback - methods Psychiatry - methods Learning Training EEG Brain–computer interface Neurofeedback Psychophysiology Neurophysiology
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Summary:	The clinical efficacy of neurofeedback is still a matter of debate. This paper analyzes the factors that should be taken into account in a transdisciplinary approach to evaluate the use of EEG NFB as a therapeutic tool in psychiatry. Neurofeedback is a neurocognitive therapy based on human-computer interaction that enables subjects to train voluntarily and modify functional biomarkers that are related to a defined mental disorder. We investigate three kinds of factors related to this definition of neurofeedback. We focus this article on EEG NFB. The first part of the paper investigates neurophysiological factors underlying the brain mechanisms driving NFB training and learning to modify a functional biomarker voluntarily. Two kinds of neuroplasticity involved in neurofeedback are analyzed: Hebbian neuroplasticity, i.e. long-term modification of neural membrane excitability and/or synaptic potentiation, and homeostatic neuroplasticity, i.e. homeostasis attempts to stabilize network activity. The second part investigates psychophysiological factors related to the targeted biomarker. It is demonstrated that neurofeedback involves clearly defining which kind of relationship between EEG biomarkers and clinical dimensions (symptoms or cognitive processes) is to be targeted. A nomenclature of accurate EEG biomarkers is proposed in the form of a short EEG encyclopedia (EEGcopia). The third part investigates human-computer interaction factors for optimizing NFB training and learning during the closed loop interaction. A model is proposed to summarize the different features that should be controlled to optimize learning. The need for accurate and reliable metrics of training and learning in line with human-computer interaction is also emphasized, including targeted biomarkers and neuroplasticity. All these factors related to neurofeedback show that it can be considered as a fertile ground for innovative research in psychiatry.
ISSN:	0013-7006
DOI:	10.1016/j.encep.2019.02.001