A Domain Adaptation Sparse Representation Classifier for Cross-Domain Electroencephalogram-Based Emotion Classification Article Swipe
Tongguang Ni
,
Yuyao Ni
,
Jing Xue
,
Suhong Wang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fpsyg.2021.721266
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fpsyg.2021.721266
The brain-computer interface (BCI) interprets the physiological information of the human brain in the process of consciousness activity. It builds a direct information transmission channel between the brain and the outside world. As the most common non-invasive BCI modality, electroencephalogram (EEG) plays an important role in the emotion recognition of BCI; however, due to the individual variability and non-stationary of EEG signals, the construction of EEG-based emotion classifiers for different subjects, different sessions, and different devices is an important research direction. Domain adaptation utilizes data or knowledge from more than one domain and focuses on transferring knowledge from the source domain (SD) to the target domain (TD), in which the EEG data may be collected from different subjects, sessions, or devices. In this study, a new domain adaptation sparse representation classifier (DASRC) is proposed to address the cross-domain EEG-based emotion classification. To reduce the differences in domain distribution, the local information preserved criterion is exploited to project the samples from SD and TD into a shared subspace. A common domain-invariant dictionary is learned in the projection subspace so that an inherent connection can be built between SD and TD. In addition, both principal component analysis (PCA) and Fisher criteria are exploited to promote the recognition ability of the learned dictionary. Besides, an optimization method is proposed to alternatively update the subspace and dictionary learning. The comparison of CSFDDL shows the feasibility and competitive performance for cross-subject and cross-dataset EEG-based emotion classification problems.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fpsyg.2021.721266
- https://www.frontiersin.org/articles/10.3389/fpsyg.2021.721266/pdf
- OA Status
- gold
- Cited By
- 10
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3186857927
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3186857927Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fpsyg.2021.721266Digital Object Identifier
- Title
-
A Domain Adaptation Sparse Representation Classifier for Cross-Domain Electroencephalogram-Based Emotion ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-07-29Full publication date if available
- Authors
-
Tongguang Ni, Yuyao Ni, Jing Xue, Suhong WangList of authors in order
- Landing page
-
https://doi.org/10.3389/fpsyg.2021.721266Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fpsyg.2021.721266/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fpsyg.2021.721266/pdfDirect OA link when available
- Concepts
-
Brain–computer interface, Electroencephalography, Computer science, Artificial intelligence, Classifier (UML), Pattern recognition (psychology), Subspace topology, Speech recognition, Principal component analysis, Machine learning, Psychology, PsychiatryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 4, 2023: 2, 2022: 3, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3186857927 |
|---|---|
| doi | https://doi.org/10.3389/fpsyg.2021.721266 |
| ids.doi | https://doi.org/10.3389/fpsyg.2021.721266 |
| ids.mag | 3186857927 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/34393958 |
| ids.openalex | https://openalex.org/W3186857927 |
| fwci | 1.34278056 |
| type | article |
| title | A Domain Adaptation Sparse Representation Classifier for Cross-Domain Electroencephalogram-Based Emotion Classification |
| biblio.issue | |
| biblio.volume | 12 |
| biblio.last_page | 721266 |
| biblio.first_page | 721266 |
| topics[0].id | https://openalex.org/T10429 |
| topics[0].field.id | https://openalex.org/fields/28 |
| topics[0].field.display_name | Neuroscience |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2805 |
| topics[0].subfield.display_name | Cognitive Neuroscience |
| topics[0].display_name | EEG and Brain-Computer Interfaces |
| topics[1].id | https://openalex.org/T10667 |
| topics[1].field.id | https://openalex.org/fields/32 |
| topics[1].field.display_name | Psychology |
| topics[1].score | 0.9993000030517578 |
| topics[1].domain.id | https://openalex.org/domains/2 |
| topics[1].domain.display_name | Social Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3205 |
| topics[1].subfield.display_name | Experimental and Cognitive Psychology |
| topics[1].display_name | Emotion and Mood Recognition |
| topics[2].id | https://openalex.org/T11021 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9812999963760376 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2705 |
| topics[2].subfield.display_name | Cardiology and Cardiovascular Medicine |
| topics[2].display_name | ECG Monitoring and Analysis |
| funders[0].id | https://openalex.org/F4320316345 |
| funders[0].ror | |
| funders[0].display_name | Project on Maternal and Child Health Talents of Jiangsu Province |
| funders[1].id | https://openalex.org/F4320336126 |
| funders[1].ror | |
| funders[1].display_name | National Natural Science Foundation of China-Henan Joint Fund |
| is_xpac | False |
| apc_list.value | 2950 |
| apc_list.currency | USD |
| apc_list.value_usd | 2950 |
| apc_paid.value | 2950 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 2950 |
| concepts[0].id | https://openalex.org/C173201364 |
| concepts[0].level | 3 |
| concepts[0].score | 0.7354813814163208 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q897410 |
| concepts[0].display_name | Brain–computer interface |
| concepts[1].id | https://openalex.org/C522805319 |
| concepts[1].level | 2 |
| concepts[1].score | 0.700065016746521 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q179965 |
| concepts[1].display_name | Electroencephalography |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6838672757148743 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.6418155431747437 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C95623464 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6395595073699951 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1096149 |
| concepts[4].display_name | Classifier (UML) |
| concepts[5].id | https://openalex.org/C153180895 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5769214630126953 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[5].display_name | Pattern recognition (psychology) |
| concepts[6].id | https://openalex.org/C32834561 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5637872815132141 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q660730 |
| concepts[6].display_name | Subspace topology |
| concepts[7].id | https://openalex.org/C28490314 |
| concepts[7].level | 1 |
| concepts[7].score | 0.5099343061447144 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q189436 |
| concepts[7].display_name | Speech recognition |
| concepts[8].id | https://openalex.org/C27438332 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4173964858055115 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2873 |
| concepts[8].display_name | Principal component analysis |
| concepts[9].id | https://openalex.org/C119857082 |
| concepts[9].level | 1 |
| concepts[9].score | 0.36970680952072144 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[9].display_name | Machine learning |
| concepts[10].id | https://openalex.org/C15744967 |
| concepts[10].level | 0 |
| concepts[10].score | 0.22431501746177673 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[10].display_name | Psychology |
| concepts[11].id | https://openalex.org/C118552586 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7867 |
| concepts[11].display_name | Psychiatry |
| keywords[0].id | https://openalex.org/keywords/brain–computer-interface |
| keywords[0].score | 0.7354813814163208 |
| keywords[0].display_name | Brain–computer interface |
| keywords[1].id | https://openalex.org/keywords/electroencephalography |
| keywords[1].score | 0.700065016746521 |
| keywords[1].display_name | Electroencephalography |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6838672757148743 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.6418155431747437 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/classifier |
| keywords[4].score | 0.6395595073699951 |
| keywords[4].display_name | Classifier (UML) |
| keywords[5].id | https://openalex.org/keywords/pattern-recognition |
| keywords[5].score | 0.5769214630126953 |
| keywords[5].display_name | Pattern recognition (psychology) |
| keywords[6].id | https://openalex.org/keywords/subspace-topology |
| keywords[6].score | 0.5637872815132141 |
| keywords[6].display_name | Subspace topology |
| keywords[7].id | https://openalex.org/keywords/speech-recognition |
| keywords[7].score | 0.5099343061447144 |
| keywords[7].display_name | Speech recognition |
| keywords[8].id | https://openalex.org/keywords/principal-component-analysis |
| keywords[8].score | 0.4173964858055115 |
| keywords[8].display_name | Principal component analysis |
| keywords[9].id | https://openalex.org/keywords/machine-learning |
| keywords[9].score | 0.36970680952072144 |
| keywords[9].display_name | Machine learning |
| keywords[10].id | https://openalex.org/keywords/psychology |
| keywords[10].score | 0.22431501746177673 |
| keywords[10].display_name | Psychology |
| language | en |
| locations[0].id | doi:10.3389/fpsyg.2021.721266 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S9692511 |
| locations[0].source.issn | 1664-1078 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1664-1078 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Frontiers in Psychology |
| locations[0].source.host_organization | https://openalex.org/P4310320527 |
| locations[0].source.host_organization_name | Frontiers Media |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320527 |
| locations[0].source.host_organization_lineage_names | Frontiers Media |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.frontiersin.org/articles/10.3389/fpsyg.2021.721266/pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Frontiers in Psychology |
| locations[0].landing_page_url | https://doi.org/10.3389/fpsyg.2021.721266 |
| locations[1].id | pmid:34393958 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Frontiers in psychology |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/34393958 |
| locations[2].id | pmh:oai:doaj.org/article:eb2e2ff8983445f69a403cee5b87891d |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | cc-by-sa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Frontiers in Psychology, Vol 12 (2021) |
| locations[2].landing_page_url | https://doaj.org/article/eb2e2ff8983445f69a403cee5b87891d |
| locations[3].id | pmh:oai:frl.publisso.de:frl:6476294 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306402606 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | Informationszentrum Lebenswissenschaften (ZB MED) |
| locations[3].source.host_organization | https://openalex.org/I4210112663 |
| locations[3].source.host_organization_name | ZB MED - Information Centre for Life Sciences |
| locations[3].source.host_organization_lineage | https://openalex.org/I4210112663 |
| locations[3].license | cc-by |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Zeitschriftenartikel |
| locations[3].license_id | https://openalex.org/licenses/cc-by |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Frontiers in psychology, 12:721266 |
| locations[3].landing_page_url | https://repository.publisso.de/resource/frl:6476294 |
| locations[4].id | pmh:oai:pubmedcentral.nih.gov:8358659 |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S2764455111 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | False |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | PubMed Central |
| locations[4].source.host_organization | https://openalex.org/I1299303238 |
| locations[4].source.host_organization_name | National Institutes of Health |
| locations[4].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[4].license | other-oa |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | Text |
| locations[4].license_id | https://openalex.org/licenses/other-oa |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Front Psychol |
| locations[4].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/8358659 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5003274219 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0354-5116 |
| authorships[0].author.display_name | Tongguang Ni |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210153482 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China |
| authorships[0].institutions[0].id | https://openalex.org/I4210153482 |
| authorships[0].institutions[0].ror | https://ror.org/04ymgwq66 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210153482 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Changzhou University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Tongguang Ni |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China |
| authorships[1].author.id | https://openalex.org/A5069263935 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Yuyao Ni |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I87445476 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, China |
| authorships[1].institutions[0].id | https://openalex.org/I87445476 |
| authorships[1].institutions[0].ror | https://ror.org/017zhmm22 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I87445476 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Xi'an Jiaotong University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yuyao Ni |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, China |
| authorships[2].author.id | https://openalex.org/A5101758042 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Jing Xue |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210158071, https://openalex.org/I83519826 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Nephrology, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China |
| authorships[2].institutions[0].id | https://openalex.org/I83519826 |
| authorships[2].institutions[0].ror | https://ror.org/059gcgy73 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I83519826 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Nanjing Medical University |
| authorships[2].institutions[1].id | https://openalex.org/I4210158071 |
| authorships[2].institutions[1].ror | https://ror.org/05pb5hm55 |
| authorships[2].institutions[1].type | healthcare |
| authorships[2].institutions[1].lineage | https://openalex.org/I4210158071 |
| authorships[2].institutions[1].country_code | CN |
| authorships[2].institutions[1].display_name | Wuxi People's Hospital |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Jing Xue |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | Department of Nephrology, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China |
| authorships[3].author.id | https://openalex.org/A5053327243 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-9526-9843 |
| authorships[3].author.display_name | Suhong Wang |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I3923682 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Clinical Psychology, The Third Affiliated Hospital of Soochow University, Changzhou, China |
| authorships[3].institutions[0].id | https://openalex.org/I3923682 |
| authorships[3].institutions[0].ror | https://ror.org/05t8y2r12 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I3923682 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Soochow University |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Suhong Wang |
| authorships[3].is_corresponding | True |
| authorships[3].raw_affiliation_strings | Department of Clinical Psychology, The Third Affiliated Hospital of Soochow University, Changzhou, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.frontiersin.org/articles/10.3389/fpsyg.2021.721266/pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Domain Adaptation Sparse Representation Classifier for Cross-Domain Electroencephalogram-Based Emotion Classification |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10429 |
| primary_topic.field.id | https://openalex.org/fields/28 |
| primary_topic.field.display_name | Neuroscience |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2805 |
| primary_topic.subfield.display_name | Cognitive Neuroscience |
| primary_topic.display_name | EEG and Brain-Computer Interfaces |
| related_works | https://openalex.org/W3202969339, https://openalex.org/W4237513258, https://openalex.org/W2044053727, https://openalex.org/W1994410349, https://openalex.org/W3177028067, https://openalex.org/W1913385466, https://openalex.org/W2914170859, https://openalex.org/W2889342546, https://openalex.org/W2015048155, https://openalex.org/W1969223073 |
| cited_by_count | 10 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 4 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 2 |
| counts_by_year[2].year | 2022 |
| counts_by_year[2].cited_by_count | 3 |
| counts_by_year[3].year | 2021 |
| counts_by_year[3].cited_by_count | 1 |
| locations_count | 5 |
| best_oa_location.id | doi:10.3389/fpsyg.2021.721266 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S9692511 |
| best_oa_location.source.issn | 1664-1078 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1664-1078 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Frontiers in Psychology |
| best_oa_location.source.host_organization | https://openalex.org/P4310320527 |
| best_oa_location.source.host_organization_name | Frontiers Media |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320527 |
| best_oa_location.source.host_organization_lineage_names | Frontiers Media |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.frontiersin.org/articles/10.3389/fpsyg.2021.721266/pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Frontiers in Psychology |
| best_oa_location.landing_page_url | https://doi.org/10.3389/fpsyg.2021.721266 |
| primary_location.id | doi:10.3389/fpsyg.2021.721266 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S9692511 |
| primary_location.source.issn | 1664-1078 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1664-1078 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Frontiers in Psychology |
| primary_location.source.host_organization | https://openalex.org/P4310320527 |
| primary_location.source.host_organization_name | Frontiers Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320527 |
| primary_location.source.host_organization_lineage_names | Frontiers Media |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.frontiersin.org/articles/10.3389/fpsyg.2021.721266/pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Frontiers in Psychology |
| primary_location.landing_page_url | https://doi.org/10.3389/fpsyg.2021.721266 |
| publication_date | 2021-07-29 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2160547390, https://openalex.org/W2611647730, https://openalex.org/W3014215018, https://openalex.org/W4239510810, https://openalex.org/W2991465589, https://openalex.org/W2902034646, https://openalex.org/W2888527098, https://openalex.org/W3038733215, https://openalex.org/W2081420711, https://openalex.org/W1963932623, https://openalex.org/W2939611082, https://openalex.org/W2002055708, https://openalex.org/W2799657112, https://openalex.org/W2982126608, https://openalex.org/W2926366943, https://openalex.org/W2790814155, https://openalex.org/W2801423304, https://openalex.org/W2999501225, https://openalex.org/W2580887161, https://openalex.org/W2073468849, https://openalex.org/W2051218759, https://openalex.org/W3094098763, https://openalex.org/W3083495197, https://openalex.org/W3048580993, https://openalex.org/W2115403315, https://openalex.org/W2965342519, https://openalex.org/W1779010541, https://openalex.org/W2292475108, https://openalex.org/W2579628011, https://openalex.org/W2963009172, https://openalex.org/W2930980596, https://openalex.org/W1947251450, https://openalex.org/W2962905870, https://openalex.org/W2119821739, https://openalex.org/W3104433444 |
| referenced_works_count | 35 |
| abstract_inverted_index.A | 167 |
| abstract_inverted_index.a | 20, 124, 164 |
| abstract_inverted_index.As | 32 |
| abstract_inverted_index.In | 121, 189 |
| abstract_inverted_index.It | 18 |
| abstract_inverted_index.SD | 160, 186 |
| abstract_inverted_index.TD | 162 |
| abstract_inverted_index.To | 141 |
| abstract_inverted_index.an | 42, 77, 179, 211 |
| abstract_inverted_index.be | 113, 183 |
| abstract_inverted_index.in | 12, 45, 107, 145, 173 |
| abstract_inverted_index.is | 76, 132, 153, 171, 214 |
| abstract_inverted_index.of | 8, 15, 49, 59, 64, 206, 226 |
| abstract_inverted_index.on | 94 |
| abstract_inverted_index.or | 85, 119 |
| abstract_inverted_index.so | 177 |
| abstract_inverted_index.to | 53, 102, 134, 155, 201, 216 |
| abstract_inverted_index.BCI | 37 |
| abstract_inverted_index.EEG | 60, 110 |
| abstract_inverted_index.TD. | 188 |
| abstract_inverted_index.The | 0, 224 |
| abstract_inverted_index.and | 28, 57, 73, 92, 161, 187, 196, 221, 231, 236 |
| abstract_inverted_index.are | 199 |
| abstract_inverted_index.can | 182 |
| abstract_inverted_index.due | 52 |
| abstract_inverted_index.for | 68, 234 |
| abstract_inverted_index.may | 112 |
| abstract_inverted_index.new | 125 |
| abstract_inverted_index.one | 90 |
| abstract_inverted_index.the | 5, 9, 13, 26, 29, 33, 46, 54, 62, 98, 103, 109, 136, 143, 148, 157, 174, 203, 207, 219, 229 |
| abstract_inverted_index.(SD) | 101 |
| abstract_inverted_index.BCI; | 50 |
| abstract_inverted_index.both | 191 |
| abstract_inverted_index.data | 84, 111 |
| abstract_inverted_index.from | 87, 97, 115, 159 |
| abstract_inverted_index.into | 163 |
| abstract_inverted_index.more | 88 |
| abstract_inverted_index.most | 34 |
| abstract_inverted_index.role | 44 |
| abstract_inverted_index.than | 89 |
| abstract_inverted_index.that | 178 |
| abstract_inverted_index.this | 122 |
| abstract_inverted_index.(BCI) | 3 |
| abstract_inverted_index.(EEG) | 40 |
| abstract_inverted_index.(PCA) | 195 |
| abstract_inverted_index.(TD), | 106 |
| abstract_inverted_index.brain | 11, 27 |
| abstract_inverted_index.built | 184 |
| abstract_inverted_index.human | 10 |
| abstract_inverted_index.local | 149 |
| abstract_inverted_index.plays | 41 |
| abstract_inverted_index.shows | 228 |
| abstract_inverted_index.which | 108 |
| abstract_inverted_index.CSFDDL | 227 |
| abstract_inverted_index.Domain | 81 |
| abstract_inverted_index.Fisher | 197 |
| abstract_inverted_index.builds | 19 |
| abstract_inverted_index.common | 35, 168 |
| abstract_inverted_index.direct | 21 |
| abstract_inverted_index.domain | 91, 100, 105, 126, 146 |
| abstract_inverted_index.method | 213 |
| abstract_inverted_index.reduce | 142 |
| abstract_inverted_index.shared | 165 |
| abstract_inverted_index.source | 99 |
| abstract_inverted_index.sparse | 128 |
| abstract_inverted_index.study, | 123 |
| abstract_inverted_index.target | 104 |
| abstract_inverted_index.update | 218 |
| abstract_inverted_index.world. | 31 |
| abstract_inverted_index.(DASRC) | 131 |
| abstract_inverted_index.ability | 205 |
| abstract_inverted_index.address | 135 |
| abstract_inverted_index.between | 25, 185 |
| abstract_inverted_index.channel | 24 |
| abstract_inverted_index.devices | 75 |
| abstract_inverted_index.emotion | 47, 66, 139, 239 |
| abstract_inverted_index.focuses | 93 |
| abstract_inverted_index.learned | 172, 208 |
| abstract_inverted_index.outside | 30 |
| abstract_inverted_index.process | 14 |
| abstract_inverted_index.project | 156 |
| abstract_inverted_index.promote | 202 |
| abstract_inverted_index.samples | 158 |
| abstract_inverted_index.Besides, | 210 |
| abstract_inverted_index.analysis | 194 |
| abstract_inverted_index.criteria | 198 |
| abstract_inverted_index.devices. | 120 |
| abstract_inverted_index.however, | 51 |
| abstract_inverted_index.inherent | 180 |
| abstract_inverted_index.proposed | 133, 215 |
| abstract_inverted_index.research | 79 |
| abstract_inverted_index.signals, | 61 |
| abstract_inverted_index.subspace | 176, 220 |
| abstract_inverted_index.utilizes | 83 |
| abstract_inverted_index.EEG-based | 65, 138, 238 |
| abstract_inverted_index.activity. | 17 |
| abstract_inverted_index.addition, | 190 |
| abstract_inverted_index.collected | 114 |
| abstract_inverted_index.component | 193 |
| abstract_inverted_index.criterion | 152 |
| abstract_inverted_index.different | 69, 71, 74, 116 |
| abstract_inverted_index.exploited | 154, 200 |
| abstract_inverted_index.important | 43, 78 |
| abstract_inverted_index.interface | 2 |
| abstract_inverted_index.knowledge | 86, 96 |
| abstract_inverted_index.learning. | 223 |
| abstract_inverted_index.modality, | 38 |
| abstract_inverted_index.preserved | 151 |
| abstract_inverted_index.principal | 192 |
| abstract_inverted_index.problems. | 241 |
| abstract_inverted_index.sessions, | 72, 118 |
| abstract_inverted_index.subjects, | 70, 117 |
| abstract_inverted_index.subspace. | 166 |
| abstract_inverted_index.adaptation | 82, 127 |
| abstract_inverted_index.classifier | 130 |
| abstract_inverted_index.comparison | 225 |
| abstract_inverted_index.connection | 181 |
| abstract_inverted_index.dictionary | 170, 222 |
| abstract_inverted_index.direction. | 80 |
| abstract_inverted_index.individual | 55 |
| abstract_inverted_index.interprets | 4 |
| abstract_inverted_index.projection | 175 |
| abstract_inverted_index.classifiers | 67 |
| abstract_inverted_index.competitive | 232 |
| abstract_inverted_index.dictionary. | 209 |
| abstract_inverted_index.differences | 144 |
| abstract_inverted_index.feasibility | 230 |
| abstract_inverted_index.information | 7, 22, 150 |
| abstract_inverted_index.performance | 233 |
| abstract_inverted_index.recognition | 48, 204 |
| abstract_inverted_index.variability | 56 |
| abstract_inverted_index.construction | 63 |
| abstract_inverted_index.cross-domain | 137 |
| abstract_inverted_index.non-invasive | 36 |
| abstract_inverted_index.optimization | 212 |
| abstract_inverted_index.transferring | 95 |
| abstract_inverted_index.transmission | 23 |
| abstract_inverted_index.alternatively | 217 |
| abstract_inverted_index.consciousness | 16 |
| abstract_inverted_index.cross-dataset | 237 |
| abstract_inverted_index.cross-subject | 235 |
| abstract_inverted_index.distribution, | 147 |
| abstract_inverted_index.physiological | 6 |
| abstract_inverted_index.brain-computer | 1 |
| abstract_inverted_index.classification | 240 |
| abstract_inverted_index.non-stationary | 58 |
| abstract_inverted_index.representation | 129 |
| abstract_inverted_index.classification. | 140 |
| abstract_inverted_index.domain-invariant | 169 |
| abstract_inverted_index.electroencephalogram | 39 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5101758042, https://openalex.org/A5053327243 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I3923682, https://openalex.org/I4210158071, https://openalex.org/I83519826 |
| citation_normalized_percentile.value | 0.77541903 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |