Subject-independent decoding of affective states using functional near-infrared spectroscopy
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,...
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| Veröffentlicht in: | PloS one 2021-01, Vol.16 (1), p.e0244840-e0244840 |
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| creator | Trambaiolli, Lucas R Tossato, Juliana Cravo, André M Biazoli, Jr, Claudinei E Sato, João R |
| description | 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 |
| doi_str_mv | 10.1371/journal.pone.0244840 |
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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±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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0244840</identifier><identifier>PMID: 33411817</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>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</subject><ispartof>PloS one, 2021-01, Vol.16 (1), p.e0244840-e0244840</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Trambaiolli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 Trambaiolli et al 2021 Trambaiolli et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-6d042fca9c3359bbdb533abed20db9b5310d2e159a29bb1c0e48e1658af93d1c3</citedby><cites>FETCH-LOGICAL-c692t-6d042fca9c3359bbdb533abed20db9b5310d2e159a29bb1c0e48e1658af93d1c3</cites><orcidid>0000-0001-7824-1929 ; 0000-0003-2483-4109</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790273/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790273/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2095,2914,23846,27903,27904,53770,53772,79347,79348</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33411817$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Wang, Zhishun</contributor><creatorcontrib>Trambaiolli, Lucas R</creatorcontrib><creatorcontrib>Tossato, Juliana</creatorcontrib><creatorcontrib>Cravo, André M</creatorcontrib><creatorcontrib>Biazoli, Jr, Claudinei E</creatorcontrib><creatorcontrib>Sato, João R</creatorcontrib><title>Subject-independent decoding of affective states using functional near-infrared spectroscopy</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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.</description><subject>Adult</subject><subject>Affect (Psychology)</subject><subject>Affect - physiology</subject><subject>Biofeedback training</subject><subject>Biology and Life Sciences</subject><subject>Brain - diagnostic imaging</subject><subject>Brain research</subject><subject>Brain-Computer Interfaces - psychology</subject><subject>Classification</subject><subject>Classifiers</subject><subject>Cognition & reasoning</subject><subject>Computer and Information Sciences</subject><subject>Datasets</subject><subject>Design</subject><subject>Discriminant Analysis</subject><subject>Emotional behavior</subject><subject>Emotional factors</subject><subject>Emotions</subject><subject>Emotions - 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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±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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33411817</pmid><doi>10.1371/journal.pone.0244840</doi><tpages>e0244840</tpages><orcidid>https://orcid.org/0000-0001-7824-1929</orcidid><orcidid>https://orcid.org/0000-0003-2483-4109</orcidid><oa>free_for_read</oa></addata></record> |
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| issn | 1932-6203 1932-6203 |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS); EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| 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 |
| title | Subject-independent decoding of affective states using functional near-infrared spectroscopy |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T20%3A59%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Subject-independent%20decoding%20of%20affective%20states%20using%20functional%20near-infrared%20spectroscopy&rft.jtitle=PloS%20one&rft.au=Trambaiolli,%20Lucas%20R&rft.date=2021-01-07&rft.volume=16&rft.issue=1&rft.spage=e0244840&rft.epage=e0244840&rft.pages=e0244840-e0244840&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0244840&rft_dat=%3Cgale_plos_%3EA650815125%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2476219349&rft_id=info:pmid/33411817&rft_galeid=A650815125&rft_doaj_id=oai_doaj_org_article_44805f4e46b845cdb44024723aae1fe8&rfr_iscdi=true |