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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\u00b13.23 years) and then tested in a completely independent one (20 participants, 24.00\u00b13.92 years). Significant balanced accuracies between classes were found for positive vs. negative (64.50 \u00b1 12.03%, p&lt;0.01) and negative vs. neutral (68.25 \u00b1 12.97%, p&lt;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 \u00b1 18.02%, p&lt;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.<\/jats:p>","DOI":"10.1371\/journal.pone.0244840","type":"journal-article","created":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T16:44:21Z","timestamp":1610037861000},"page":"e0244840","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":10,"title":["Subject-independent decoding of affective states using functional near-infrared spectroscopy"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7824-1929","authenticated-orcid":true,"given":"Lucas R.","family":"Trambaiolli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juliana","family":"Tossato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9 M.","family":"Cravo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2483-4109","authenticated-orcid":true,"given":"Claudinei E.","family":"Biazoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o R.","family":"Sato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,1,7]]},"reference":[{"issue":"7","key":"pone.0244840.ref001","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1038\/nrn1931","article-title":"Neuroimaging: decoding mental states from brain activity in humans","volume":"7","author":"J-D Haynes","year":"2006","journal-title":"Nature Reviews Neuroscience"},{"key":"pone.0244840.ref002","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.cortex.2015.01.020","article-title":"Brain-decoding fMRI reveals how wholes relate to the sum of parts","volume":"72","author":"J Kubilius","year":"2015","journal-title":"Cortex"},{"issue":"2","key":"pone.0244840.ref003","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1080\/2326263X.2014.912881","article-title":"A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges","volume":"1","author":"C M\u00fchl","year":"2014","journal-title":"Brain-Computer Interfaces"},{"issue":"5","key":"pone.0244840.ref004","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1097\/YCO.0000000000000087","article-title":"Real-time functional MRI neurofeedback: a tool for psychiatry","volume":"27","author":"S Kim","year":"2014","journal-title":"Current opinion in psychiatry"},{"issue":"2","key":"pone.0244840.ref005","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.encep.2016.11.003","article-title":"Neurofeedback: One of today\u2019s techniques in psychiatry?","volume":"43","author":"M Arns","year":"2017","journal-title":"L\u2019Enc\u00e9phale"},{"key":"pone.0244840.ref006","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.neuroimage.2013.03.033","article-title":"Real-time fMRI neurofeedback: progress and challenges","volume":"76","author":"J Sulzer","year":"2013","journal-title":"Neuroimage"},{"issue":"2","key":"pone.0244840.ref007","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1109\/T-AFFC.2013.11","article-title":"Exploring cross-modality affective reactions for audiovisual emotion recognition","volume":"4","author":"S Mariooryad","year":"2013","journal-title":"IEEE Transactions on affective computing"},{"issue":"1","key":"pone.0244840.ref008","doi-asserted-by":"crossref","first-page":"e0146691","DOI":"10.1371\/journal.pone.0146691","article-title":"Recognition of intensive valence and arousal affective states via facial electromyographic activity in young and senior adults","volume":"11","author":"J-W Tan","year":"2016","journal-title":"PloS one"},{"issue":"7","key":"pone.0244840.ref009","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TBME.2010.2048568","article-title":"EEG-based emotion recognition in music listening","volume":"57","author":"Y-P Lin","year":"2010","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"pone.0244840.ref010","doi-asserted-by":"crossref","unstructured":"Bozhkov L, Georgieva P, editors. 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