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

Autor(es)

Trambaiolli, Lucas R. ^[1] ; Tossato, Juliana ^[2] ; Cravo, Andre M. ^[2] ; Biazoli, Jr., Claudinei E. ^[2] ; Sato, Joao R. ^[2]

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

• 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
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 18/04654-9 - Séries temporais, ondaletas e dados de alta dimensão
• Beneficiário: Pedro Alberto Morettin
• Modalidade de apoio: 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
• Modalidade de apoio: 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
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica