Ontology type: schema:ScholarlyArticle
2022-04-13
AUTHORSSantosh Kumar Sahoo, Sumant Kumar Mohapatra
ABSTRACTBrain Computer Interface (BCI) recommended for online real-time processing of EEG signals. Hence, the recording system’s accuracy improved by nullifying of developed artifacts. The goal of the proposed work is to develop an optimized model for detecting and minimizing ocular artifacts. In the proposed work, Discrete Wavelet Transform (DWT) and Pisarenko harmonic decomposition are used for decomposing the signals. Then the features are extracted by Principal Component Analysis (PCA) and Independent Component Analysis (ICA) techniques. After feature collection, an Optimized Deformable Convolutional Network (ODCN) is used for the recognition of ocular artifacts from EEG input signals. When artifacts are sensed, the moderation method is executed by applying Empirical Mean Curve Decomposition (EMCD) followed by ODCN for noise optimization in EEG signals. Conclusively, the spotless signal is reconstructed by an application of inverse EMCD. The proposed method has achieved a higher performance than that of conventional methods. More... »
PAGES1-15
http://scigraph.springernature.com/pub.10.1007/s12652-022-03783-3
DOIhttp://dx.doi.org/10.1007/s12652-022-03783-3
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1147059595
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Department of Electronics & Instrumentation Engineering, CVR College of Engineering, Hyderabad, Telangana, India",
"id": "http://www.grid.ac/institutes/None",
"name": [
"Department of Electronics & Instrumentation Engineering, CVR College of Engineering, Hyderabad, Telangana, India"
],
"type": "Organization"
},
"familyName": "Sahoo",
"givenName": "Santosh Kumar",
"id": "sg:person.010035744161.20",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010035744161.20"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Electronics and Telecommunication Engineering, Trident Academy of Technology, Bhubaneswar, Odisha, India",
"id": "http://www.grid.ac/institutes/None",
"name": [
"Department of Electronics and Telecommunication Engineering, Trident Academy of Technology, Bhubaneswar, Odisha, India"
],
"type": "Organization"
},
"familyName": "Mohapatra",
"givenName": "Sumant Kumar",
"id": "sg:person.011370005262.23",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011370005262.23"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/s00521-019-04641-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1123151483",
"https://doi.org/10.1007/s00521-019-04641-8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-981-13-3600-3_76",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1111455606",
"https://doi.org/10.1007/978-981-13-3600-3_76"
],
"type": "CreativeWork"
}
],
"datePublished": "2022-04-13",
"datePublishedReg": "2022-04-13",
"description": "Brain Computer Interface (BCI) recommended\u00a0for\u00a0online real-time processing of EEG signals. Hence, the\u00a0recording system\u2019s accuracy improved by nullifying of developed artifacts. The goal of the proposed work\u00a0is to develop an optimized\u00a0model for\u00a0detecting and minimizing ocular artifacts. In\u00a0the proposed work,\u00a0Discrete Wavelet Transform (DWT) and Pisarenko harmonic decomposition are used for decomposing the signals. Then the features are extracted by Principal Component Analysis (PCA) and Independent Component Analysis (ICA) techniques. After feature\u00a0collection, an Optimized Deformable Convolutional Network (ODCN) is used for the recognition of ocular artifacts from EEG input signals. When artifacts are sensed, the moderation method is executed by applying Empirical Mean Curve Decomposition (EMCD) followed by ODCN for noise optimization in EEG signals. Conclusively, the spotless signal is reconstructed by an application of inverse EMCD. The proposed method has achieved a higher performance than that of conventional methods.",
"genre": "article",
"id": "sg:pub.10.1007/s12652-022-03783-3",
"inLanguage": "en",
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1043999",
"issn": [
"1868-5137",
"1868-5145"
],
"name": "Journal of Ambient Intelligence and Humanized Computing",
"publisher": "Springer Nature",
"type": "Periodical"
}
],
"keywords": [
"empirical mean curve decomposition",
"discrete wavelet transform",
"ocular artifacts",
"online real-time processing",
"noise optimization",
"real-time processing",
"wavelet transform",
"independent component analysis (ICA) technique",
"input signal",
"Pisarenko harmonic decomposition",
"high performance",
"system accuracy",
"EEG signals",
"harmonic decomposition",
"conventional methods",
"component analysis technique",
"brain-computer interface",
"signals",
"analysis techniques",
"decomposition",
"moderation method",
"accuracy",
"interface",
"method",
"transform",
"optimization",
"computer interface",
"work",
"curve decomposition",
"performance",
"principal component analysis",
"applications",
"processing",
"realization",
"scheme",
"convolutional network",
"artifacts",
"technique",
"model",
"features",
"component analysis",
"network",
"analysis",
"deformable convolutional networks",
"goal",
"collection",
"recognition"
],
"name": "Ocular Artifacts realization through optimized scheme",
"pagination": "1-15",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1147059595"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s12652-022-03783-3"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s12652-022-03783-3",
"https://app.dimensions.ai/details/publication/pub.1147059595"
],
"sdDataset": "articles",
"sdDatePublished": "2022-06-01T22:24",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_937.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s12652-022-03783-3"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s12652-022-03783-3'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12652-022-03783-3'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12652-022-03783-3'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12652-022-03783-3'
This table displays all metadata directly associated to this object as RDF triples.
116 TRIPLES
22 PREDICATES
72 URIs
62 LITERALS
4 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/s12652-022-03783-3 | schema:about | anzsrc-for:08 |
2 | ″ | ″ | anzsrc-for:0801 |
3 | ″ | schema:author | N97947bb44fff4008988c0903fccc0497 |
4 | ″ | schema:citation | sg:pub.10.1007/978-981-13-3600-3_76 |
5 | ″ | ″ | sg:pub.10.1007/s00521-019-04641-8 |
6 | ″ | schema:datePublished | 2022-04-13 |
7 | ″ | schema:datePublishedReg | 2022-04-13 |
8 | ″ | schema:description | Brain Computer Interface (BCI) recommended for online real-time processing of EEG signals. Hence, the recording system’s accuracy improved by nullifying of developed artifacts. The goal of the proposed work is to develop an optimized model for detecting and minimizing ocular artifacts. In the proposed work, Discrete Wavelet Transform (DWT) and Pisarenko harmonic decomposition are used for decomposing the signals. Then the features are extracted by Principal Component Analysis (PCA) and Independent Component Analysis (ICA) techniques. After feature collection, an Optimized Deformable Convolutional Network (ODCN) is used for the recognition of ocular artifacts from EEG input signals. When artifacts are sensed, the moderation method is executed by applying Empirical Mean Curve Decomposition (EMCD) followed by ODCN for noise optimization in EEG signals. Conclusively, the spotless signal is reconstructed by an application of inverse EMCD. The proposed method has achieved a higher performance than that of conventional methods. |
9 | ″ | schema:genre | article |
10 | ″ | schema:inLanguage | en |
11 | ″ | schema:isAccessibleForFree | false |
12 | ″ | schema:isPartOf | sg:journal.1043999 |
13 | ″ | schema:keywords | EEG signals |
14 | ″ | ″ | Pisarenko harmonic decomposition |
15 | ″ | ″ | accuracy |
16 | ″ | ″ | analysis |
17 | ″ | ″ | analysis techniques |
18 | ″ | ″ | applications |
19 | ″ | ″ | artifacts |
20 | ″ | ″ | brain-computer interface |
21 | ″ | ″ | collection |
22 | ″ | ″ | component analysis |
23 | ″ | ″ | component analysis technique |
24 | ″ | ″ | computer interface |
25 | ″ | ″ | conventional methods |
26 | ″ | ″ | convolutional network |
27 | ″ | ″ | curve decomposition |
28 | ″ | ″ | decomposition |
29 | ″ | ″ | deformable convolutional networks |
30 | ″ | ″ | discrete wavelet transform |
31 | ″ | ″ | empirical mean curve decomposition |
32 | ″ | ″ | features |
33 | ″ | ″ | goal |
34 | ″ | ″ | harmonic decomposition |
35 | ″ | ″ | high performance |
36 | ″ | ″ | independent component analysis (ICA) technique |
37 | ″ | ″ | input signal |
38 | ″ | ″ | interface |
39 | ″ | ″ | method |
40 | ″ | ″ | model |
41 | ″ | ″ | moderation method |
42 | ″ | ″ | network |
43 | ″ | ″ | noise optimization |
44 | ″ | ″ | ocular artifacts |
45 | ″ | ″ | online real-time processing |
46 | ″ | ″ | optimization |
47 | ″ | ″ | performance |
48 | ″ | ″ | principal component analysis |
49 | ″ | ″ | processing |
50 | ″ | ″ | real-time processing |
51 | ″ | ″ | realization |
52 | ″ | ″ | recognition |
53 | ″ | ″ | scheme |
54 | ″ | ″ | signals |
55 | ″ | ″ | system accuracy |
56 | ″ | ″ | technique |
57 | ″ | ″ | transform |
58 | ″ | ″ | wavelet transform |
59 | ″ | ″ | work |
60 | ″ | schema:name | Ocular Artifacts realization through optimized scheme |
61 | ″ | schema:pagination | 1-15 |
62 | ″ | schema:productId | N8fa0dacb6a254aea947e8ee9568946c8 |
63 | ″ | ″ | Nad8c633918ec4b86863f3b86eb89f997 |
64 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1147059595 |
65 | ″ | ″ | https://doi.org/10.1007/s12652-022-03783-3 |
66 | ″ | schema:sdDatePublished | 2022-06-01T22:24 |
67 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
68 | ″ | schema:sdPublisher | N5749dda67c6c493e8bf420d64df5c932 |
69 | ″ | schema:url | https://doi.org/10.1007/s12652-022-03783-3 |
70 | ″ | sgo:license | sg:explorer/license/ |
71 | ″ | sgo:sdDataset | articles |
72 | ″ | rdf:type | schema:ScholarlyArticle |
73 | N5749dda67c6c493e8bf420d64df5c932 | schema:name | Springer Nature - SN SciGraph project |
74 | ″ | rdf:type | schema:Organization |
75 | N8fa0dacb6a254aea947e8ee9568946c8 | schema:name | doi |
76 | ″ | schema:value | 10.1007/s12652-022-03783-3 |
77 | ″ | rdf:type | schema:PropertyValue |
78 | N97947bb44fff4008988c0903fccc0497 | rdf:first | sg:person.010035744161.20 |
79 | ″ | rdf:rest | Ne2af1aed1f6a4ba99623e5ab2930ebb5 |
80 | Nad8c633918ec4b86863f3b86eb89f997 | schema:name | dimensions_id |
81 | ″ | schema:value | pub.1147059595 |
82 | ″ | rdf:type | schema:PropertyValue |
83 | Ne2af1aed1f6a4ba99623e5ab2930ebb5 | rdf:first | sg:person.011370005262.23 |
84 | ″ | rdf:rest | rdf:nil |
85 | anzsrc-for:08 | schema:inDefinedTermSet | anzsrc-for: |
86 | ″ | schema:name | Information and Computing Sciences |
87 | ″ | rdf:type | schema:DefinedTerm |
88 | anzsrc-for:0801 | schema:inDefinedTermSet | anzsrc-for: |
89 | ″ | schema:name | Artificial Intelligence and Image Processing |
90 | ″ | rdf:type | schema:DefinedTerm |
91 | sg:journal.1043999 | schema:issn | 1868-5137 |
92 | ″ | ″ | 1868-5145 |
93 | ″ | schema:name | Journal of Ambient Intelligence and Humanized Computing |
94 | ″ | schema:publisher | Springer Nature |
95 | ″ | rdf:type | schema:Periodical |
96 | sg:person.010035744161.20 | schema:affiliation | grid-institutes:None |
97 | ″ | schema:familyName | Sahoo |
98 | ″ | schema:givenName | Santosh Kumar |
99 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010035744161.20 |
100 | ″ | rdf:type | schema:Person |
101 | sg:person.011370005262.23 | schema:affiliation | grid-institutes:None |
102 | ″ | schema:familyName | Mohapatra |
103 | ″ | schema:givenName | Sumant Kumar |
104 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011370005262.23 |
105 | ″ | rdf:type | schema:Person |
106 | sg:pub.10.1007/978-981-13-3600-3_76 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1111455606 |
107 | ″ | ″ | https://doi.org/10.1007/978-981-13-3600-3_76 |
108 | ″ | rdf:type | schema:CreativeWork |
109 | sg:pub.10.1007/s00521-019-04641-8 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1123151483 |
110 | ″ | ″ | https://doi.org/10.1007/s00521-019-04641-8 |
111 | ″ | rdf:type | schema:CreativeWork |
112 | grid-institutes:None | schema:alternateName | Department of Electronics & Instrumentation Engineering, CVR College of Engineering, Hyderabad, Telangana, India |
113 | ″ | ″ | Department of Electronics and Telecommunication Engineering, Trident Academy of Technology, Bhubaneswar, Odisha, India |
114 | ″ | schema:name | Department of Electronics & Instrumentation Engineering, CVR College of Engineering, Hyderabad, Telangana, India |
115 | ″ | ″ | Department of Electronics and Telecommunication Engineering, Trident Academy of Technology, Bhubaneswar, Odisha, India |
116 | ″ | rdf:type | schema:Organization |