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Multi-modal Virtual Scenario Enhances Neurofeedback Learning

Cohen, Avihay, Keynan, Jackob Nimrod, Jackont, Gilan, Green, Nili, Rashap, Iris, Shany, Ofir, Charles, Fred, Cavazza, Marc, Hendler, Talma, Raz, Gal and others. (2016) Multi-modal Virtual Scenario Enhances Neurofeedback Learning. Frontiers in Robotics and AI, 3 . p. 52. ISSN 2296-9144. E-ISSN 2296-9144. (doi:10.3389/frobt.2016.00052)

Abstract

In the past decade neurofeedback (NF) has become the focus of a growing body of

research. With real-time functional magnetic resonance imaging (fMRI) enabling online

monitoring of emotion-related areas, such as the amygdala, many have begun testing

its therapeutic benefits. However, most existing NF procedures still use monotonic unimodal

interfaces, thus possibly limiting user engagement and weakening learning efficiency.

The current study tested a novel multi-sensory NF animated scenario (AS) aimed

at enhancing user experience and improving learning. We examined whether relative to

a simple uni-modal 2D interface, learning via an interface of complex multi-modal 3D

scenario will result in improved NF learning. As a neural-probe, we used the recently

developed fMRI-inspired EEG model of amygdala activity (“amygdala-EEG finger print”;

amygdala-EFP), enabling low-cost and mobile limbic NF training. Amygdala-EFP was

reflected in the AS by the unrest level of a hospital waiting room in which virtual characters

become impatient, approach the admission desk and complain loudly. Successful

downregulation was reflected as an ease in the room unrest level. We tested whether

relative to a standard uni-modal 2D graphic thermometer (TM) interface, this AS could

facilitate more effective learning and improve the training experience. Thirty participants

underwent two separated NF sessions (1 week apart) practicing downregulation of

the amygdala-EFP signal. In the first session, half trained via the AS and half via a TM

interface. Learning efficiency was tested by three parameters: (a) effect size of the

change in amygdala-EFP following training, (b) sustainability of the learned downregulation

in the absence of online feedback, and (c) transferability to an unfamiliar context.

Comparing amygdala-EFP signal amplitude between the last and the first NF trials

revealed that the AS produced a higher effect size. In addition, NF via the AS showed

better sustainability, as indicated by a no-feedback trial conducted in session 2 and

better transferability to a new unfamiliar interface. Lastly, participants reported that

the AS was more engaging and more motivating than the TM. Together, these results

demonstrate the promising potential of integrating realistic virtual environments in NF

to enhance learning and improve user’s experience.

Item Type: Article
DOI/Identification number: 10.3389/frobt.2016.00052
Uncontrolled keywords: EEG–fMRI integration, EEG-neurofeedback, fMRI-neurofeedback, real-time fMRI, amygdala, emotion regulation, User Interface, Virtual Reality
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.575 Multimedia systems
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.9.H85 Human computer interaction
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Depositing User: Marc Cavazza
Date Deposited: 12 Sep 2016 14:48 UTC
Last Modified: 01 Aug 2019 10:40 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/57238 (The current URI for this page, for reference purposes)
Cavazza, Marc: https://orcid.org/0000-0001-6113-9696
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