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

• Published in: PloS one Vol. 16; no. 1; p. e0244840
• Main Authors: Trambaiolli, Lucas R., Tossato, Juliana, Cravo, André M., Biazoli, Claudinei E., Sato, João R.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.01.2021; Public Library of Science (PLoS)
• Subjects: Adult; Affect (Psychology); Affect - physiology; Biofeedback training; Biology and Life Sciences; Brain - diagnostic imaging; Brain research; Brain-Computer Interfaces - psychology; Classification; Classifiers; Cognition & reasoning; Computer and Information Sciences; Datasets; Design; Discriminant Analysis; Emotional behavior; Emotional factors; Emotions; Emotions - physiology; Engineering and Technology; Ethics; Feedback; Female; Frontal Lobe - diagnostic imaging; Functional Neuroimaging - methods; Humans; I.R. radiation; Infrared spectra; Infrared spectroscopy; Male; Mathematics; Medical imaging; Medical research; Medicine and Health Sciences; Methods; Near infrared radiation; Near infrared spectroscopy; Neural networks; Neurofeedback - methods; Neuroimaging; Neurosciences; Occipital Lobe - diagnostic imaging; Physical Sciences; Rehabilitation; Research and Analysis Methods; Sensors; Social Sciences; Spectroscopy, Near-Infrared - methods; Spectrum analysis; Values; United States; Brazil; Los Angeles California; California; United States > US
• DOI: 10.1371/journal.pone.0244840
• ISSN: 1932-6203

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.