Literature DB >> 33382801

Are the new mobile wireless EEG headsets reliable for the evaluation of musical pleasure?

Thibault Chabin1, Damien Gabriel1,2,3, Emmanuel Haffen1,2,3, Thierry Moulin1,2,3, Lionel Pazart1,2,3.   

Abstract

Since the beginning of the 20th century, electroencephalography (EEG) has been used in a wide variety of applications, both for medical needs and for the study of various cerebral processes. With the rapid development of the technique, more and more precise and advanced tools have emerged for research purposes. However, the main constraints of these devices have often been the high price and, for some devices the low transportability and the long set-up time. Nevertheless, a broad range of wireless EEG devices have emerged on the market without these constraints, but with a lower signal quality. The development of EEG recording on multiple participants simultaneously, and new technological solutions provides further possibilities to understand the cerebral emotional dynamics of a group. A great number of studies have compared and tested many mobile devices, but have provided contradictory results. It is therefore important to test the reliability of specific wireless devices in a specific research context before developing a large-scale study. The aim of this study was to assess the reliability of two wireless devices (g.tech Nautilus SAHARA electrodes and Emotiv™ Epoc +) for the detection of musical emotions, in comparison with a gold standard EEG device. Sixteen participants reported feeling emotional pleasure (from low pleasure up to musical chills) when listening to their favorite chill-inducing musical excerpts. In terms of emotion detection, our results show statistically significant concordance between Epoc + and the gold standard device in the left prefrontal and left temporal areas in the alpha frequency band. We validated the use of the Emotiv™ Epoc + for research into musical emotion. We did not find any significant concordance between g.tech and the gold standard. This suggests that Emotiv Epoc is more appropriate for musical emotion investigations in natural settings.

Entities:  

Year:  2020        PMID: 33382801      PMCID: PMC7775075          DOI: 10.1371/journal.pone.0244820

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  51 in total

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Authors:  Yuan-Pin Lin; Jeng-Ren Duann; Jyh-Horng Chen; Tzyy-Ping Jung
Journal:  Neuroreport       Date:  2010-04-21       Impact factor: 1.837

2.  Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music.

Authors:  Daniela Sammler; Maren Grigutsch; Thomas Fritz; Stefan Koelsch
Journal:  Psychophysiology       Date:  2007-03       Impact factor: 4.016

3.  [Hans Berger (1873-1941)--the history of electroencephalography].

Authors:  Mario Tudor; Lorainne Tudor; Katarina Ivana Tudor
Journal:  Acta Med Croatica       Date:  2005

4.  Brain-to-Brain Synchrony Tracks Real-World Dynamic Group Interactions in the Classroom.

Authors:  Suzanne Dikker; Lu Wan; Ido Davidesco; Lisa Kaggen; Matthias Oostrik; James McClintock; Jess Rowland; Georgios Michalareas; Jay J Van Bavel; Mingzhou Ding; David Poeppel
Journal:  Curr Biol       Date:  2017-04-27       Impact factor: 10.834

5.  Spatiotemporal analysis of multichannel EEG: CARTOOL.

Authors:  Denis Brunet; Micah M Murray; Christoph M Michel
Journal:  Comput Intell Neurosci       Date:  2011-01-05

6.  French validation of the Barcelona Music Reward Questionnaire.

Authors:  Joe Saliba; Urbano Lorenzo-Seva; Josep Marco-Pallares; Barbara Tillmann; Anthony Zeitouni; Alexandre Lehmann
Journal:  PeerJ       Date:  2016-03-21       Impact factor: 2.984

7.  Signal Quality Evaluation of Emerging EEG Devices.

Authors:  Thea Radüntz
Journal:  Front Physiol       Date:  2018-02-14       Impact factor: 4.566

8.  A novel Brain Computer Interface for classification of social joint attention in autism and comparison of 3 experimental setups: A feasibility study.

Authors:  Carlos P Amaral; Marco A Simões; Susana Mouga; João Andrade; Miguel Castelo-Branco
Journal:  J Neurosci Methods       Date:  2017-07-29       Impact factor: 2.390

Review 9.  Categorisation of Mobile EEG: A Researcher's Perspective.

Authors:  Anthony D Bateson; Heidi A Baseler; Kevin S Paulson; Fayyaz Ahmed; Aziz U R Asghar
Journal:  Biomed Res Int       Date:  2017-12-04       Impact factor: 3.411

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  1 in total

1.  Human state anxiety classification framework using EEG signals in response to exposure therapy.

Authors:  Farah Muhammad; Saad Al-Ahmadi
Journal:  PLoS One       Date:  2022-03-18       Impact factor: 3.240

  1 in total

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