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  Classification of complex emotions using EEG and virtual environment: Proof of concept and therapeutic implication

De Filippi, E., Wolter, M., Melo, B. R. P., Tierra-Criollo, C. J., Bortolini, T., Deco, G., et al. (2021). Classification of complex emotions using EEG and virtual environment: Proof of concept and therapeutic implication. Frontiers in Human Neuroscience, 15: 711279. doi:10.3389/fnhum.2021.711279.

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 Creators:
De Filippi, Eleonora1, Author
Wolter, Mara2, Author
Melo, Bruno R. P.2, 3, Author
Tierra-Criollo, Carlos J.3, Author
Bortolini, Tiago2, Author
Deco, Gustavo1, 4, 5, 6, Author              
Moll, Jorge2, 7, Author
Affiliations:
1Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Spain, ou_persistent22              
2Cognitive and Behavioral Neuroscience Unit, D'Or Institute for Research and Education, Rio de Janeiro, Brazil, ou_persistent22              
3Biomedical Engineering Program, Federal University of Rio de Janeiro, Brazil, ou_persistent22              
4Catalan Institution for Research and Advanced Studies (ICREA), University Pompeu Fabra, Barcelona, Spain, ou_persistent22              
5Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634551              
6Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia, ou_persistent22              
7Scients Institute, Palo Alto, CA, United States, ou_persistent22              

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Free keywords: Emotions; Electroencephalography; Classification; Machine-learning; Neuro-feedback; Multimodal virtual scenario
 Abstract: During the last decades, neurofeedback training for emotional self-regulation has received significant attention from scientific and clinical communities. Most studies have investigated emotions using functional magnetic resonance imaging (fMRI), including the real-time application in neurofeedback training. However, the electroencephalogram (EEG) is a more suitable tool for therapeutic application. Our study aims at establishing a method to classify discrete complex emotions (e.g., tenderness and anguish) elicited through a near-immersive scenario that can be later used for EEG-neurofeedback. EEG-based affective computing studies have mainly focused on emotion classification based on dimensions, commonly using passive elicitation through single-modality stimuli. Here, we integrated both passive and active elicitation methods. We recorded electrophysiological data during emotion-evoking trials, combining emotional self-induction with a multimodal virtual environment. We extracted correlational and time-frequency features, including frontal-alpha asymmetry (FAA), using Complex Morlet Wavelet convolution. Thinking about future real-time applications, we performed within-subject classification using 1-s windows as samples and we applied trial-specific cross-validation. We opted for a traditional machine-learning classifier with low computational complexity and sufficient validation in online settings, the Support Vector Machine. Results of individual-based cross-validation using the whole feature sets showed considerable between-subject variability. The individual accuracies ranged from 59.2 to 92.9% using time-frequency/FAA and 62.4 to 92.4% using correlational features. We found that features of the temporal, occipital, and left-frontal channels were the most discriminative between the two emotions. Our results show that the suggested pipeline is suitable for individual-based classification of discrete emotions, paving the way for future personalized EEG-neurofeedback training.

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Language(s): eng - English
 Dates: 2021-05-182021-07-292021-08-26
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.3389/fnhum.2021.711279
Other: eCollection 2021
PMID: 34512297
PMC: PMC8427812
 Degree: -

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Grant ID : E-26/ 202.962/2015
Funding program : -
Funding organization : Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)

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Title: Frontiers in Human Neuroscience
  Abbreviation : Front Hum Neurosci
Source Genre: Journal
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Publ. Info: Lausanne, Switzerland : Frontiers Research Foundation
Pages: - Volume / Issue: 15 Sequence Number: 711279 Start / End Page: - Identifier: ISSN: 1662-5161
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5161