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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Subject-independent decoding of affective states using functional near-infrared spectroscopy

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Autor(es):
Trambaiolli, Lucas R. [1] ; Tossato, Juliana [2] ; Cravo, Andre M. [2] ; Biazoli, Jr., Claudinei E. [2] ; Sato, Joao R. [2]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Harvard Med Sch, McLean Hosp, Div Basic Neurosci, Boston, MA 02115 - USA
[2] Fed Univ ABC, Ctr Math Comp & Cognit, Sao Bernardo Do Campo, SP - Brazil
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: PLoS One; v. 16, n. 1 JAN 7 2021.
Citações Web of Science: 0
Resumo

Affective decoding is the inference of human emotional states using brain signal measurements. This approach is crucial to develop new therapeutic approaches for psychiatric rehabilitation, such as affective neurofeedback protocols. To reduce the training duration and optimize the clinical outputs, an ideal clinical neurofeedback could be trained using data from an independent group of volunteers before being used by new patients. Here, we investigated if this subject-independent design of affective decoding can be achieved using functional near-infrared spectroscopy (fNIRS) signals from frontal and occipital areas. For this purpose, a linear discriminant analysis classifier was first trained in a dataset (49 participants, 24.65 +/- 3.23 years) and then tested in a completely independent one (20 participants, 24.00 +/- 3.92 years). Significant balanced accuracies between classes were found for positive vs. negative (64.50 +/- 12.03%, p<0.01) and negative vs. neutral (68.25 +/- 12.97%, p<0.01) affective states discrimination during a reactive block consisting in viewing affective-loaded images. For an active block, in which volunteers were instructed to recollect personal affective experiences, significant accuracy was found for positive vs. neutral affect classification (71.25 +/- 18.02%, p<0.01). In this last case, only three fNIRS channels were enough to discriminate between neutral and positive affective states. Although more research is needed, for example focusing on better combinations of features and classifiers, our results highlight fNIRS as a possible technique for subject-independent affective decoding, reaching significant classification accuracies of emotional states using only a few but biologically relevant features. (AU)

Processo FAPESP: 18/21934-5 - Estatística de redes: teoria, métodos e aplicações
Beneficiário:André Fujita
Linha de fomento: Auxílio à Pesquisa - Temático
Processo FAPESP: 18/04654-9 - Séries temporais, ondaletas e dados de alta dimensão
Beneficiário:Pedro Alberto Morettin
Linha de fomento: Auxílio à Pesquisa - Temático
Processo FAPESP: 15/17406-5 - Decodificação emocional e neuromodulação do córtex prefrontal com registros simultâneo NIRS-EEG
Beneficiário:Lucas Remoaldo Trambaiolli
Linha de fomento: Bolsas no Brasil - Doutorado
Processo FAPESP: 17/05225-1 - Decodificação de valência e intensidade afetivas a partir de sinais hemodinâmicos do córtex pré-frontal
Beneficiário:Juliana França Tossato
Linha de fomento: Bolsas no Brasil - Iniciação Científica