s-SMOOTH: Sparsity and Smoothness Enhanced EEG Brain Tomography Article Swipe
Ying Li
,
Jing Qin
,
Yue-Loong Hsin
,
Stanley Osher
,
Wentai Liu
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2016.00543
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2016.00543
EEG source imaging enables us to reconstruct current density in the brain from the electrical measurements with excellent temporal resolution (~ ms). The corresponding EEG inverse problem is an ill-posed one that has infinitely many solutions. This is due to the fact that the number of EEG sensors is usually much smaller than that of the potential dipole locations, as well as noise contamination in the recorded signals. To obtain a unique solution, regularizations can be incorporated to impose additional constraints on the solution. An appropriate choice of regularization is critically important for the reconstruction accuracy of a brain image. In this paper, we propose a novel Sparsity and SMOOthness enhanced brain TomograpHy (s-SMOOTH) method to improve the reconstruction accuracy by integrating two recently proposed regularization techniques: Total Generalized Variation (TGV) regularization and ℓ1-2 regularization. TGV is able to preserve the source edge and recover the spatial distribution of the source intensity with high accuracy. Compared to the relevant total variation (TV) regularization, TGV enhances the smoothness of the image and reduces staircasing artifacts. The traditional TGV defined on a 2D image has been widely used in the image processing field. In order to handle 3D EEG source images, we propose a voxel-based Total Generalized Variation (vTGV) regularization that extends the definition of second-order TGV from 2D planar images to 3D irregular surfaces such as cortex surface. In addition, the ℓ1-2 regularization is utilized to promote sparsity on the current density itself. We demonstrate that ℓ1-2 regularization is able to enhance sparsity and accelerate computations than ℓ1 regularization. The proposed model is solved by an efficient and robust algorithm based on the difference of convex functions algorithm (DCA) and the alternating direction method of multipliers (ADMM). Numerical experiments using synthetic data demonstrate the advantages of the proposed method over other state-of-the-art methods in terms of total reconstruction accuracy, localization accuracy and focalization degree. The application to the source localization of event-related potential data further demonstrates the performance of the proposed method in real-world scenarios.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnins.2016.00543
- OA Status
- gold
- Cited By
- 19
- References
- 65
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2558937877
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2558937877Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fnins.2016.00543Digital Object Identifier
- Title
-
s-SMOOTH: Sparsity and Smoothness Enhanced EEG Brain TomographyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-11-28Full publication date if available
- Authors
-
Ying Li, Jing Qin, Yue-Loong Hsin, Stanley Osher, Wentai LiuList of authors in order
- Landing page
-
https://doi.org/10.3389/fnins.2016.00543Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3389/fnins.2016.00543Direct OA link when available
- Concepts
-
Regularization (linguistics), Inverse problem, Computer science, Smoothness, Electroencephalography, Algorithm, Iterative reconstruction, Artificial intelligence, Computer vision, Mathematics, Mathematical analysis, Neuroscience, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2022: 5, 2021: 1, 2020: 1, 2019: 2Per-year citation counts (last 5 years)
- References (count)
-
65Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2558937877 |
|---|---|
| doi | https://doi.org/10.3389/fnins.2016.00543 |
| ids.doi | https://doi.org/10.3389/fnins.2016.00543 |
| ids.mag | 2558937877 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/27965529 |
| ids.openalex | https://openalex.org/W2558937877 |
| fwci | 3.30931954 |
| type | article |
| title | s-SMOOTH: Sparsity and Smoothness Enhanced EEG Brain Tomography |
| biblio.issue | |
| biblio.volume | 10 |
| biblio.last_page | 543 |
| biblio.first_page | 543 |
| topics[0].id | https://openalex.org/T10500 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9994999766349792 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2206 |
| topics[0].subfield.display_name | Computational Mechanics |
| topics[0].display_name | Sparse and Compressive Sensing Techniques |
| topics[1].id | https://openalex.org/T10378 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9987000226974487 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2741 |
| topics[1].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[1].display_name | Advanced MRI Techniques and Applications |
| topics[2].id | https://openalex.org/T10241 |
| topics[2].field.id | https://openalex.org/fields/28 |
| topics[2].field.display_name | Neuroscience |
| topics[2].score | 0.9987000226974487 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2805 |
| topics[2].subfield.display_name | Cognitive Neuroscience |
| topics[2].display_name | Functional Brain Connectivity Studies |
| funders[0].id | https://openalex.org/F4320306159 |
| funders[0].ror | https://ror.org/000dswa46 |
| funders[0].display_name | W. M. Keck Foundation |
| funders[1].id | https://openalex.org/F4320337246 |
| funders[1].ror | |
| funders[1].display_name | Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation |
| 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/C2776135515 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7979816198348999 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q17143721 |
| concepts[0].display_name | Regularization (linguistics) |
| concepts[1].id | https://openalex.org/C135252773 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6538251042366028 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1567213 |
| concepts[1].display_name | Inverse problem |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5720281004905701 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C102634674 |
| concepts[3].level | 2 |
| concepts[3].score | 0.4848529100418091 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q868473 |
| concepts[3].display_name | Smoothness |
| concepts[4].id | https://openalex.org/C522805319 |
| concepts[4].level | 2 |
| concepts[4].score | 0.46796971559524536 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q179965 |
| concepts[4].display_name | Electroencephalography |
| concepts[5].id | https://openalex.org/C11413529 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4364531636238098 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[5].display_name | Algorithm |
| concepts[6].id | https://openalex.org/C141379421 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4104927182197571 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q6094427 |
| concepts[6].display_name | Iterative reconstruction |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.4103671610355377 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C31972630 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3751172423362732 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[8].display_name | Computer vision |
| concepts[9].id | https://openalex.org/C33923547 |
| concepts[9].level | 0 |
| concepts[9].score | 0.30787068605422974 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[9].display_name | Mathematics |
| concepts[10].id | https://openalex.org/C134306372 |
| concepts[10].level | 1 |
| concepts[10].score | 0.10453736782073975 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[10].display_name | Mathematical analysis |
| concepts[11].id | https://openalex.org/C169760540 |
| concepts[11].level | 1 |
| concepts[11].score | 0.09683847427368164 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[11].display_name | Neuroscience |
| concepts[12].id | https://openalex.org/C86803240 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[12].display_name | Biology |
| keywords[0].id | https://openalex.org/keywords/regularization |
| keywords[0].score | 0.7979816198348999 |
| keywords[0].display_name | Regularization (linguistics) |
| keywords[1].id | https://openalex.org/keywords/inverse-problem |
| keywords[1].score | 0.6538251042366028 |
| keywords[1].display_name | Inverse problem |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.5720281004905701 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/smoothness |
| keywords[3].score | 0.4848529100418091 |
| keywords[3].display_name | Smoothness |
| keywords[4].id | https://openalex.org/keywords/electroencephalography |
| keywords[4].score | 0.46796971559524536 |
| keywords[4].display_name | Electroencephalography |
| keywords[5].id | https://openalex.org/keywords/algorithm |
| keywords[5].score | 0.4364531636238098 |
| keywords[5].display_name | Algorithm |
| keywords[6].id | https://openalex.org/keywords/iterative-reconstruction |
| keywords[6].score | 0.4104927182197571 |
| keywords[6].display_name | Iterative reconstruction |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.4103671610355377 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/computer-vision |
| keywords[8].score | 0.3751172423362732 |
| keywords[8].display_name | Computer vision |
| keywords[9].id | https://openalex.org/keywords/mathematics |
| keywords[9].score | 0.30787068605422974 |
| keywords[9].display_name | Mathematics |
| keywords[10].id | https://openalex.org/keywords/mathematical-analysis |
| keywords[10].score | 0.10453736782073975 |
| keywords[10].display_name | Mathematical analysis |
| keywords[11].id | https://openalex.org/keywords/neuroscience |
| keywords[11].score | 0.09683847427368164 |
| keywords[11].display_name | Neuroscience |
| language | en |
| locations[0].id | doi:10.3389/fnins.2016.00543 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S115201632 |
| locations[0].source.issn | 1662-453X, 1662-4548 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1662-453X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Frontiers in Neuroscience |
| 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 | |
| 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 Neuroscience |
| locations[0].landing_page_url | https://doi.org/10.3389/fnins.2016.00543 |
| locations[1].id | pmid:27965529 |
| 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 neuroscience |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/27965529 |
| locations[2].id | pmh:oai:doaj.org/article:ff3f6dc2381942caa1fbe14664246e1c |
| 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 Neuroscience, Vol 10 (2016) |
| locations[2].landing_page_url | https://doaj.org/article/ff3f6dc2381942caa1fbe14664246e1c |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:5125305 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| 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 | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | cc-by |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| 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 | Front Neurosci |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/5125305 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5100414156 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-1487-3350 |
| authorships[0].author.display_name | Ying Li |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I161318765 |
| authorships[0].affiliations[0].raw_affiliation_string | Biomimetic Research Lab, Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA |
| authorships[0].institutions[0].id | https://openalex.org/I161318765 |
| authorships[0].institutions[0].ror | https://ror.org/046rm7j60 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I161318765 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | University of California, Los Angeles |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Ying Li |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Biomimetic Research Lab, Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA |
| authorships[1].author.id | https://openalex.org/A5066509235 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-8630-2904 |
| authorships[1].author.display_name | Jing Qin |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I23732399 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA |
| authorships[1].institutions[0].id | https://openalex.org/I23732399 |
| authorships[1].institutions[0].ror | https://ror.org/02w0trx84 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I23732399, https://openalex.org/I4210126032 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Montana State University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jing Qin |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA |
| authorships[2].author.id | https://openalex.org/A5112228399 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Yue-Loong Hsin |
| authorships[2].countries | TW |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I91279580 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Neurology, Chung Shan Medical University, Taichung, Taiwan |
| authorships[2].institutions[0].id | https://openalex.org/I91279580 |
| authorships[2].institutions[0].ror | https://ror.org/059ryjv25 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I91279580 |
| authorships[2].institutions[0].country_code | TW |
| authorships[2].institutions[0].display_name | Chung Shan Medical University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Yue-Loong Hsin |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Neurology, Chung Shan Medical University, Taichung, Taiwan |
| authorships[3].author.id | https://openalex.org/A5002037883 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-7900-4658 |
| authorships[3].author.display_name | Stanley Osher |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I161318765 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Mathematics, University of California, Los Angeles, Los Angeles, CA, USA |
| authorships[3].institutions[0].id | https://openalex.org/I161318765 |
| authorships[3].institutions[0].ror | https://ror.org/046rm7j60 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I161318765 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | University of California, Los Angeles |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Stanley Osher |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Mathematics, University of California, Los Angeles, Los Angeles, CA, USA |
| authorships[4].author.id | https://openalex.org/A5009313373 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-9845-8743 |
| authorships[4].author.display_name | Wentai Liu |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I142207940, https://openalex.org/I161318765 |
| authorships[4].affiliations[0].raw_affiliation_string | California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, USA |
| authorships[4].affiliations[1].institution_ids | https://openalex.org/I161318765 |
| authorships[4].affiliations[1].raw_affiliation_string | Biomimetic Research Lab, Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA |
| authorships[4].institutions[0].id | https://openalex.org/I142207940 |
| authorships[4].institutions[0].ror | https://ror.org/00q7fqf35 |
| authorships[4].institutions[0].type | facility |
| authorships[4].institutions[0].lineage | https://openalex.org/I142207940, https://openalex.org/I154570441, https://openalex.org/I161318765 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | California NanoSystems Institute |
| authorships[4].institutions[1].id | https://openalex.org/I161318765 |
| authorships[4].institutions[1].ror | https://ror.org/046rm7j60 |
| authorships[4].institutions[1].type | education |
| authorships[4].institutions[1].lineage | https://openalex.org/I161318765 |
| authorships[4].institutions[1].country_code | US |
| authorships[4].institutions[1].display_name | University of California, Los Angeles |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Wentai Liu |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Biomimetic Research Lab, Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, USA |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.3389/fnins.2016.00543 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | s-SMOOTH: Sparsity and Smoothness Enhanced EEG Brain Tomography |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10500 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9994999766349792 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2206 |
| primary_topic.subfield.display_name | Computational Mechanics |
| primary_topic.display_name | Sparse and Compressive Sensing Techniques |
| related_works | https://openalex.org/W1965977581, https://openalex.org/W2061980133, https://openalex.org/W2050855072, https://openalex.org/W2077506191, https://openalex.org/W2387685679, https://openalex.org/W3134728064, https://openalex.org/W2115238236, https://openalex.org/W2374214022, https://openalex.org/W2347781941, https://openalex.org/W4298004047 |
| cited_by_count | 19 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2022 |
| counts_by_year[1].cited_by_count | 5 |
| counts_by_year[2].year | 2021 |
| counts_by_year[2].cited_by_count | 1 |
| counts_by_year[3].year | 2020 |
| counts_by_year[3].cited_by_count | 1 |
| counts_by_year[4].year | 2019 |
| counts_by_year[4].cited_by_count | 2 |
| counts_by_year[5].year | 2018 |
| counts_by_year[5].cited_by_count | 4 |
| counts_by_year[6].year | 2017 |
| counts_by_year[6].cited_by_count | 5 |
| locations_count | 4 |
| best_oa_location.id | doi:10.3389/fnins.2016.00543 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S115201632 |
| best_oa_location.source.issn | 1662-453X, 1662-4548 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1662-453X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Frontiers in Neuroscience |
| 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 | |
| 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 Neuroscience |
| best_oa_location.landing_page_url | https://doi.org/10.3389/fnins.2016.00543 |
| primary_location.id | doi:10.3389/fnins.2016.00543 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S115201632 |
| primary_location.source.issn | 1662-453X, 1662-4548 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1662-453X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Frontiers in Neuroscience |
| 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 | |
| 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 Neuroscience |
| primary_location.landing_page_url | https://doi.org/10.3389/fnins.2016.00543 |
| publication_date | 2016-11-28 |
| publication_year | 2016 |
| referenced_works | https://openalex.org/W2337912789, https://openalex.org/W1989944217, https://openalex.org/W2100391382, https://openalex.org/W2155143074, https://openalex.org/W2016980059, https://openalex.org/W6712058356, https://openalex.org/W1991789140, https://openalex.org/W2149625672, https://openalex.org/W2000594266, https://openalex.org/W2146157652, https://openalex.org/W2031425398, https://openalex.org/W2071841247, https://openalex.org/W2085300014, https://openalex.org/W2004886822, https://openalex.org/W2415123130, https://openalex.org/W2082371233, https://openalex.org/W2101979660, https://openalex.org/W2155136387, https://openalex.org/W1985453570, https://openalex.org/W2084333685, https://openalex.org/W2007364043, https://openalex.org/W2012883158, https://openalex.org/W2127655853, https://openalex.org/W2107775155, https://openalex.org/W2113030459, https://openalex.org/W1970163214, https://openalex.org/W2083003685, https://openalex.org/W2009814490, https://openalex.org/W1973798575, https://openalex.org/W1991857131, https://openalex.org/W2027190915, https://openalex.org/W1981934003, https://openalex.org/W245658, https://openalex.org/W2165411673, https://openalex.org/W2001428189, https://openalex.org/W2007891950, https://openalex.org/W2017408522, https://openalex.org/W2027700252, https://openalex.org/W4250543537, https://openalex.org/W2148146191, https://openalex.org/W2166073443, https://openalex.org/W2088434677, https://openalex.org/W2512603352, https://openalex.org/W2964004468, https://openalex.org/W6600012500, https://openalex.org/W2037420423, https://openalex.org/W1971820868, https://openalex.org/W2158021845, https://openalex.org/W1916477561, https://openalex.org/W1987333243, https://openalex.org/W1990493255, https://openalex.org/W6716051074, https://openalex.org/W2406007819, https://openalex.org/W2025099118, https://openalex.org/W2145742001, https://openalex.org/W2091421749, https://openalex.org/W2954799503, https://openalex.org/W2317475341, https://openalex.org/W2089106993, https://openalex.org/W2029560486, https://openalex.org/W2026408836, https://openalex.org/W604355987, https://openalex.org/W639628635, https://openalex.org/W2146545011, https://openalex.org/W3098015273 |
| referenced_works_count | 65 |
| abstract_inverted_index.a | 70, 97, 105, 179, 201 |
| abstract_inverted_index.(~ | 20 |
| abstract_inverted_index.2D | 180, 216 |
| abstract_inverted_index.3D | 195, 220 |
| abstract_inverted_index.An | 84 |
| abstract_inverted_index.In | 100, 191, 227 |
| abstract_inverted_index.To | 68 |
| abstract_inverted_index.We | 242 |
| abstract_inverted_index.an | 28, 264 |
| abstract_inverted_index.as | 59, 61, 224 |
| abstract_inverted_index.be | 75 |
| abstract_inverted_index.by | 120, 263 |
| abstract_inverted_index.in | 9, 64, 186, 302, 331 |
| abstract_inverted_index.is | 27, 37, 48, 89, 136, 232, 247, 261 |
| abstract_inverted_index.of | 45, 54, 87, 96, 148, 167, 212, 273, 283, 294, 304, 319, 327 |
| abstract_inverted_index.on | 81, 178, 237, 270 |
| abstract_inverted_index.to | 5, 39, 77, 115, 138, 156, 193, 219, 234, 249, 315 |
| abstract_inverted_index.us | 4 |
| abstract_inverted_index.we | 103, 199 |
| abstract_inverted_index.EEG | 0, 24, 46, 196 |
| abstract_inverted_index.TGV | 135, 163, 176, 214 |
| abstract_inverted_index.The | 22, 174, 258, 313 |
| abstract_inverted_index.and | 108, 132, 143, 170, 252, 266, 278, 310 |
| abstract_inverted_index.can | 74 |
| abstract_inverted_index.due | 38 |
| abstract_inverted_index.for | 92 |
| abstract_inverted_index.has | 32, 182 |
| abstract_inverted_index.one | 30 |
| abstract_inverted_index.the | 10, 13, 40, 43, 55, 65, 82, 93, 117, 140, 145, 149, 157, 165, 168, 187, 210, 229, 238, 271, 279, 292, 295, 316, 325, 328 |
| abstract_inverted_index.two | 122 |
| abstract_inverted_index.(TV) | 161 |
| abstract_inverted_index.This | 36 |
| abstract_inverted_index.able | 137, 248 |
| abstract_inverted_index.been | 183 |
| abstract_inverted_index.data | 290, 322 |
| abstract_inverted_index.edge | 142 |
| abstract_inverted_index.fact | 41 |
| abstract_inverted_index.from | 12, 215 |
| abstract_inverted_index.high | 153 |
| abstract_inverted_index.many | 34 |
| abstract_inverted_index.much | 50 |
| abstract_inverted_index.over | 298 |
| abstract_inverted_index.such | 223 |
| abstract_inverted_index.than | 52, 255 |
| abstract_inverted_index.that | 31, 42, 53, 208, 244 |
| abstract_inverted_index.this | 101 |
| abstract_inverted_index.used | 185 |
| abstract_inverted_index.well | 60 |
| abstract_inverted_index.with | 16, 152 |
| abstract_inverted_index.(DCA) | 277 |
| abstract_inverted_index.(TGV) | 130 |
| abstract_inverted_index.Total | 127, 203 |
| abstract_inverted_index.based | 269 |
| abstract_inverted_index.brain | 11, 98, 111 |
| abstract_inverted_index.image | 169, 181, 188 |
| abstract_inverted_index.model | 260 |
| abstract_inverted_index.noise | 62 |
| abstract_inverted_index.novel | 106 |
| abstract_inverted_index.order | 192 |
| abstract_inverted_index.other | 299 |
| abstract_inverted_index.terms | 303 |
| abstract_inverted_index.total | 159, 305 |
| abstract_inverted_index.using | 288 |
| abstract_inverted_index.(vTGV) | 206 |
| abstract_inverted_index.choice | 86 |
| abstract_inverted_index.convex | 274 |
| abstract_inverted_index.cortex | 225 |
| abstract_inverted_index.dipole | 57 |
| abstract_inverted_index.field. | 190 |
| abstract_inverted_index.handle | 194 |
| abstract_inverted_index.image. | 99 |
| abstract_inverted_index.images | 218 |
| abstract_inverted_index.impose | 78 |
| abstract_inverted_index.method | 114, 282, 297, 330 |
| abstract_inverted_index.number | 44 |
| abstract_inverted_index.obtain | 69 |
| abstract_inverted_index.paper, | 102 |
| abstract_inverted_index.planar | 217 |
| abstract_inverted_index.robust | 267 |
| abstract_inverted_index.solved | 262 |
| abstract_inverted_index.source | 1, 141, 150, 197, 317 |
| abstract_inverted_index.unique | 71 |
| abstract_inverted_index.widely | 184 |
| abstract_inverted_index.(ADMM). | 285 |
| abstract_inverted_index.current | 7, 239 |
| abstract_inverted_index.defined | 177 |
| abstract_inverted_index.degree. | 312 |
| abstract_inverted_index.density | 8, 240 |
| abstract_inverted_index.enables | 3 |
| abstract_inverted_index.enhance | 250 |
| abstract_inverted_index.extends | 209 |
| abstract_inverted_index.further | 323 |
| abstract_inverted_index.images, | 198 |
| abstract_inverted_index.imaging | 2 |
| abstract_inverted_index.improve | 116 |
| abstract_inverted_index.inverse | 25 |
| abstract_inverted_index.itself. | 241 |
| abstract_inverted_index.methods | 301 |
| abstract_inverted_index.problem | 26 |
| abstract_inverted_index.promote | 235 |
| abstract_inverted_index.propose | 104, 200 |
| abstract_inverted_index.recover | 144 |
| abstract_inverted_index.reduces | 171 |
| abstract_inverted_index.sensors | 47 |
| abstract_inverted_index.smaller | 51 |
| abstract_inverted_index.spatial | 146 |
| abstract_inverted_index.usually | 49 |
| abstract_inverted_index.Compared | 155 |
| abstract_inverted_index.Sparsity | 107 |
| abstract_inverted_index.accuracy | 95, 119, 309 |
| abstract_inverted_index.enhanced | 110 |
| abstract_inverted_index.enhances | 164 |
| abstract_inverted_index.preserve | 139 |
| abstract_inverted_index.proposed | 124, 259, 296, 329 |
| abstract_inverted_index.recently | 123 |
| abstract_inverted_index.recorded | 66 |
| abstract_inverted_index.relevant | 158 |
| abstract_inverted_index.signals. | 67 |
| abstract_inverted_index.sparsity | 236, 251 |
| abstract_inverted_index.surface. | 226 |
| abstract_inverted_index.surfaces | 222 |
| abstract_inverted_index.temporal | 18 |
| abstract_inverted_index.utilized | 233 |
| abstract_inverted_index.Numerical | 286 |
| abstract_inverted_index.Variation | 129, 205 |
| abstract_inverted_index.accuracy, | 307 |
| abstract_inverted_index.accuracy. | 154 |
| abstract_inverted_index.addition, | 228 |
| abstract_inverted_index.algorithm | 268, 276 |
| abstract_inverted_index.direction | 281 |
| abstract_inverted_index.efficient | 265 |
| abstract_inverted_index.excellent | 17 |
| abstract_inverted_index.functions | 275 |
| abstract_inverted_index.ill-posed | 29 |
| abstract_inverted_index.important | 91 |
| abstract_inverted_index.intensity | 151 |
| abstract_inverted_index.irregular | 221 |
| abstract_inverted_index.potential | 56, 321 |
| abstract_inverted_index.solution, | 72 |
| abstract_inverted_index.solution. | 83 |
| abstract_inverted_index.synthetic | 289 |
| abstract_inverted_index.variation | 160 |
| abstract_inverted_index.(s-SMOOTH) | 113 |
| abstract_inverted_index.SMOOthness | 109 |
| abstract_inverted_index.TomograpHy | 112 |
| abstract_inverted_index.accelerate | 253 |
| abstract_inverted_index.additional | 79 |
| abstract_inverted_index.advantages | 293 |
| abstract_inverted_index.artifacts. | 173 |
| abstract_inverted_index.critically | 90 |
| abstract_inverted_index.definition | 211 |
| abstract_inverted_index.difference | 272 |
| abstract_inverted_index.electrical | 14 |
| abstract_inverted_index.infinitely | 33 |
| abstract_inverted_index.locations, | 58 |
| abstract_inverted_index.processing | 189 |
| abstract_inverted_index.real-world | 332 |
| abstract_inverted_index.resolution | 19 |
| abstract_inverted_index.scenarios. | 333 |
| abstract_inverted_index.smoothness | 166 |
| abstract_inverted_index.solutions. | 35 |
| abstract_inverted_index.<i>ms</i>). | 21 |
| abstract_inverted_index.Generalized | 128, 204 |
| abstract_inverted_index.alternating | 280 |
| abstract_inverted_index.application | 314 |
| abstract_inverted_index.appropriate | 85 |
| abstract_inverted_index.constraints | 80 |
| abstract_inverted_index.demonstrate | 243, 291 |
| abstract_inverted_index.experiments | 287 |
| abstract_inverted_index.integrating | 121 |
| abstract_inverted_index.multipliers | 284 |
| abstract_inverted_index.performance | 326 |
| abstract_inverted_index.reconstruct | 6 |
| abstract_inverted_index.staircasing | 172 |
| abstract_inverted_index.techniques: | 126 |
| abstract_inverted_index.traditional | 175 |
| abstract_inverted_index.voxel-based | 202 |
| abstract_inverted_index.computations | 254 |
| abstract_inverted_index.demonstrates | 324 |
| abstract_inverted_index.distribution | 147 |
| abstract_inverted_index.focalization | 311 |
| abstract_inverted_index.incorporated | 76 |
| abstract_inverted_index.localization | 308, 318 |
| abstract_inverted_index.measurements | 15 |
| abstract_inverted_index.second-order | 213 |
| abstract_inverted_index.contamination | 63 |
| abstract_inverted_index.corresponding | 23 |
| abstract_inverted_index.event-related | 320 |
| abstract_inverted_index.reconstruction | 94, 118, 306 |
| abstract_inverted_index.regularization | 88, 125, 131, 207, 231, 246 |
| abstract_inverted_index.regularization, | 162 |
| abstract_inverted_index.regularization. | 134, 257 |
| abstract_inverted_index.regularizations | 73 |
| abstract_inverted_index.ℓ<sub>1</sub> | 256 |
| abstract_inverted_index.state-of-the-art | 300 |
| abstract_inverted_index.ℓ<sub>1-2</sub> | 133, 230, 245 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5100414156 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I161318765 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5899999737739563 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.92545485 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |