Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces Article Swipe
Aya Khalaf
,
Alaa-Allah Essam
,
Аhmed Ibrahim
,
Ashar Zanqour
,
Mariam El-Saqa
,
Sohila Mohamed
,
Ayman Anwar
,
Ayman El‐Baz
,
Murat Akçakaya
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.09.15.613144
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.09.15.613144
Brain-computer interfaces (BCIs) exploit brain activity to bypass neuromuscular control with the aim of providing alternative means of communication with the surrounding environment. Such systems can significantly improve the quality of life for patients suffering from severe motor or speech impairment. Multimodal BCIs have been introduced recently to enhance the performance of BCIs utilizing single modality. In this paper, we aim to improve the performance of multimodal BCIs combining Electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD). The BCIs included in the study utilized two different paradigms to infer user intent including motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. Filter Bank Common Spatial Pattern (FBCSP) algorithm was used to extract features from the EEG data. Several time series features were extracted from the envelope of the fTCD signals. Wilcoxon rank sum test and linear kernel Support vector machines (SVM) were used for feature selection and classification respectively. Additionally, a probabilistic Bayesian fusion approach was used to fuse the information from EEG and fTCD modalities. Average accuracies of 94.53%, 94.9% and 96.29% were achieved for right arm MI versus baseline, left arm MI versus baseline, and right arm MI versus left arm MI respectively. Whereas average accuracies of 95.27%, 85.93% and 96.97% were achieved for MR versus baseline, WG versus baseline, and MR versus WG respectively. Our results show that EEG- fTCD BCIs with the proposed analysis techniques outperformed the multimodal EEG-fNRIS BCIs in comparison.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.09.15.613144
- OA Status
- green
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402630720
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4402630720Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2024.09.15.613144Digital Object Identifier
- Title
-
Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfacesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-19Full publication date if available
- Authors
-
Aya Khalaf, Alaa-Allah Essam, Аhmed Ibrahim, Ashar Zanqour, Mariam El-Saqa, Sohila Mohamed, Ayman Anwar, Ayman El‐Baz, Murat AkçakayaList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.09.15.613144Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1101/2024.09.15.613144Direct OA link when available
- Concepts
-
Electroencephalography, Filter bank, Brain–computer interface, Spatial filter, Envelope (radar), Computer science, Filter (signal processing), Pattern recognition (psychology), Artificial intelligence, Speech recognition, Computer vision, Psychology, Neuroscience, Telecommunications, RadarTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
54Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4402630720 |
|---|---|
| doi | https://doi.org/10.1101/2024.09.15.613144 |
| ids.doi | https://doi.org/10.1101/2024.09.15.613144 |
| ids.openalex | https://openalex.org/W4402630720 |
| fwci | 0.0 |
| type | preprint |
| title | Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| 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 | 0.974399983882904 |
| 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/T10320 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9203000068664551 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Neural Networks and Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C522805319 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6672321557998657 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q179965 |
| concepts[0].display_name | Electroencephalography |
| concepts[1].id | https://openalex.org/C100515483 |
| concepts[1].level | 3 |
| concepts[1].score | 0.5793655514717102 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q3268235 |
| concepts[1].display_name | Filter bank |
| concepts[2].id | https://openalex.org/C173201364 |
| concepts[2].level | 3 |
| concepts[2].score | 0.57024085521698 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q897410 |
| concepts[2].display_name | Brain–computer interface |
| concepts[3].id | https://openalex.org/C121475858 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5585626363754272 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2735911 |
| concepts[3].display_name | Spatial filter |
| concepts[4].id | https://openalex.org/C65155139 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5405020713806152 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q5380912 |
| concepts[4].display_name | Envelope (radar) |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.526667058467865 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C106131492 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5110269784927368 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q3072260 |
| concepts[6].display_name | Filter (signal processing) |
| concepts[7].id | https://openalex.org/C153180895 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4744602143764496 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[7].display_name | Pattern recognition (psychology) |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4365038275718689 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C28490314 |
| concepts[9].level | 1 |
| concepts[9].score | 0.3953555226325989 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q189436 |
| concepts[9].display_name | Speech recognition |
| concepts[10].id | https://openalex.org/C31972630 |
| concepts[10].level | 1 |
| concepts[10].score | 0.25556930899620056 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[10].display_name | Computer vision |
| concepts[11].id | https://openalex.org/C15744967 |
| concepts[11].level | 0 |
| concepts[11].score | 0.25485074520111084 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[11].display_name | Psychology |
| concepts[12].id | https://openalex.org/C169760540 |
| concepts[12].level | 1 |
| concepts[12].score | 0.2426052987575531 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[12].display_name | Neuroscience |
| concepts[13].id | https://openalex.org/C76155785 |
| concepts[13].level | 1 |
| concepts[13].score | 0.17401009798049927 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[13].display_name | Telecommunications |
| concepts[14].id | https://openalex.org/C554190296 |
| concepts[14].level | 2 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q47528 |
| concepts[14].display_name | Radar |
| keywords[0].id | https://openalex.org/keywords/electroencephalography |
| keywords[0].score | 0.6672321557998657 |
| keywords[0].display_name | Electroencephalography |
| keywords[1].id | https://openalex.org/keywords/filter-bank |
| keywords[1].score | 0.5793655514717102 |
| keywords[1].display_name | Filter bank |
| keywords[2].id | https://openalex.org/keywords/brain–computer-interface |
| keywords[2].score | 0.57024085521698 |
| keywords[2].display_name | Brain–computer interface |
| keywords[3].id | https://openalex.org/keywords/spatial-filter |
| keywords[3].score | 0.5585626363754272 |
| keywords[3].display_name | Spatial filter |
| keywords[4].id | https://openalex.org/keywords/envelope |
| keywords[4].score | 0.5405020713806152 |
| keywords[4].display_name | Envelope (radar) |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.526667058467865 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/filter |
| keywords[6].score | 0.5110269784927368 |
| keywords[6].display_name | Filter (signal processing) |
| keywords[7].id | https://openalex.org/keywords/pattern-recognition |
| keywords[7].score | 0.4744602143764496 |
| keywords[7].display_name | Pattern recognition (psychology) |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.4365038275718689 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/speech-recognition |
| keywords[9].score | 0.3953555226325989 |
| keywords[9].display_name | Speech recognition |
| keywords[10].id | https://openalex.org/keywords/computer-vision |
| keywords[10].score | 0.25556930899620056 |
| keywords[10].display_name | Computer vision |
| keywords[11].id | https://openalex.org/keywords/psychology |
| keywords[11].score | 0.25485074520111084 |
| keywords[11].display_name | Psychology |
| keywords[12].id | https://openalex.org/keywords/neuroscience |
| keywords[12].score | 0.2426052987575531 |
| keywords[12].display_name | Neuroscience |
| keywords[13].id | https://openalex.org/keywords/telecommunications |
| keywords[13].score | 0.17401009798049927 |
| keywords[13].display_name | Telecommunications |
| language | en |
| locations[0].id | doi:10.1101/2024.09.15.613144 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402567 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| locations[0].source.host_organization | https://openalex.org/I2750212522 |
| locations[0].source.host_organization_name | Cold Spring Harbor Laboratory |
| locations[0].source.host_organization_lineage | https://openalex.org/I2750212522 |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.1101/2024.09.15.613144 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5027821813 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-3736-580X |
| authorships[0].author.display_name | Aya Khalaf |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Aya Khalaf |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5114229385 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Alaa-Allah Essam |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Alaa-Allah Essam |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5022362806 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-1807-2139 |
| authorships[2].author.display_name | Аhmed Ibrahim |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Ammar Ibrahim |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5107605339 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Ashar Zanqour |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Ashar Zanqour |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5107376112 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Mariam El-Saqa |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Mariam El-Saqa |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5113389792 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Sohila Mohamed |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Sohila Mohamed |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5077896434 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-4139-8321 |
| authorships[6].author.display_name | Ayman Anwar |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Ayman Anwar |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5075057705 |
| authorships[7].author.orcid | https://orcid.org/0000-0001-7264-1323 |
| authorships[7].author.display_name | Ayman El‐Baz |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Ayman Eldeib |
| authorships[7].is_corresponding | False |
| authorships[8].author.id | https://openalex.org/A5007822978 |
| authorships[8].author.orcid | https://orcid.org/0000-0001-5094-1931 |
| authorships[8].author.display_name | Murat Akçakaya |
| authorships[8].author_position | last |
| authorships[8].raw_author_name | Murat Akcakaya |
| authorships[8].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1101/2024.09.15.613144 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2024-09-20T00:00:00 |
| display_name | Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces |
| has_fulltext | False |
| 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 | 0.974399983882904 |
| 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/W2889342546, https://openalex.org/W2904049950, https://openalex.org/W1577723570, https://openalex.org/W3217252117 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1101/2024.09.15.613144 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402567 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| best_oa_location.source.host_organization | https://openalex.org/I2750212522 |
| best_oa_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.1101/2024.09.15.613144 |
| primary_location.id | doi:10.1101/2024.09.15.613144 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402567 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| primary_location.source.host_organization | https://openalex.org/I2750212522 |
| primary_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| primary_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.1101/2024.09.15.613144 |
| publication_date | 2024-09-19 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2128909182, https://openalex.org/W3111359640, https://openalex.org/W2783339817, https://openalex.org/W2947357661, https://openalex.org/W2956278931, https://openalex.org/W2796397123, https://openalex.org/W2792707772, https://openalex.org/W2016148716, https://openalex.org/W2460299357, https://openalex.org/W3010955800, https://openalex.org/W2075647286, https://openalex.org/W2032808202, https://openalex.org/W2045561515, https://openalex.org/W2057099110, https://openalex.org/W1974687042, https://openalex.org/W2087831542, https://openalex.org/W3061027672, https://openalex.org/W1561758268, https://openalex.org/W2738926490, https://openalex.org/W2602962260, https://openalex.org/W2015655161, https://openalex.org/W2231596722, https://openalex.org/W2588765333, https://openalex.org/W2792264530, https://openalex.org/W2048090360, https://openalex.org/W2764253291, https://openalex.org/W2164655776, https://openalex.org/W2883645800, https://openalex.org/W2906412950, https://openalex.org/W2932574921, https://openalex.org/W2920410445, https://openalex.org/W3082298437, https://openalex.org/W2964596665, https://openalex.org/W1969878365, https://openalex.org/W2132360759, https://openalex.org/W1967837194, https://openalex.org/W2166073443, https://openalex.org/W2099509424, https://openalex.org/W2152863712, https://openalex.org/W3012101784, https://openalex.org/W2955569539, https://openalex.org/W2913907236, https://openalex.org/W2987033705, https://openalex.org/W2170124654, https://openalex.org/W2155617375, https://openalex.org/W4301229630, https://openalex.org/W2172000360, https://openalex.org/W3153920005, https://openalex.org/W2076456816, https://openalex.org/W2047401359, https://openalex.org/W2150275893, https://openalex.org/W2089589969, https://openalex.org/W2594217161, https://openalex.org/W3039489162 |
| referenced_works_count | 54 |
| abstract_inverted_index.a | 153 |
| abstract_inverted_index.In | 57 |
| abstract_inverted_index.MI | 181, 186, 192, 196 |
| abstract_inverted_index.MR | 209, 216 |
| abstract_inverted_index.WG | 212, 218 |
| abstract_inverted_index.in | 81, 237 |
| abstract_inverted_index.of | 14, 18, 31, 52, 66, 129, 171, 201 |
| abstract_inverted_index.or | 39 |
| abstract_inverted_index.to | 7, 48, 62, 88, 113, 160 |
| abstract_inverted_index.we | 60 |
| abstract_inverted_index.EEG | 118, 165 |
| abstract_inverted_index.Our | 220 |
| abstract_inverted_index.The | 78 |
| abstract_inverted_index.aim | 13, 61 |
| abstract_inverted_index.and | 72, 96, 137, 149, 166, 174, 189, 204, 215 |
| abstract_inverted_index.arm | 180, 185, 191, 195 |
| abstract_inverted_index.can | 26 |
| abstract_inverted_index.for | 33, 146, 178, 208 |
| abstract_inverted_index.sum | 135 |
| abstract_inverted_index.the | 12, 21, 29, 50, 64, 82, 117, 127, 130, 162, 228, 233 |
| abstract_inverted_index.two | 85 |
| abstract_inverted_index.was | 111, 158 |
| abstract_inverted_index.(MI) | 95 |
| abstract_inverted_index.(WG) | 102 |
| abstract_inverted_index.BCIs | 43, 53, 68, 79, 226, 236 |
| abstract_inverted_index.Bank | 105 |
| abstract_inverted_index.EEG- | 224 |
| abstract_inverted_index.Such | 24 |
| abstract_inverted_index.been | 45 |
| abstract_inverted_index.fTCD | 131, 167, 225 |
| abstract_inverted_index.from | 36, 116, 126, 164 |
| abstract_inverted_index.fuse | 161 |
| abstract_inverted_index.have | 44 |
| abstract_inverted_index.left | 184, 194 |
| abstract_inverted_index.life | 32 |
| abstract_inverted_index.rank | 134 |
| abstract_inverted_index.show | 222 |
| abstract_inverted_index.test | 136 |
| abstract_inverted_index.that | 223 |
| abstract_inverted_index.this | 58 |
| abstract_inverted_index.time | 121 |
| abstract_inverted_index.used | 112, 145, 159 |
| abstract_inverted_index.user | 90 |
| abstract_inverted_index.were | 124, 144, 176, 206 |
| abstract_inverted_index.with | 11, 20, 227 |
| abstract_inverted_index.(EEG) | 71 |
| abstract_inverted_index.(SVM) | 143 |
| abstract_inverted_index.94.9% | 173 |
| abstract_inverted_index.brain | 5 |
| abstract_inverted_index.data. | 119 |
| abstract_inverted_index.infer | 89 |
| abstract_inverted_index.means | 17 |
| abstract_inverted_index.motor | 38, 93 |
| abstract_inverted_index.right | 179, 190 |
| abstract_inverted_index.study | 83 |
| abstract_inverted_index.(BCIs) | 3 |
| abstract_inverted_index.85.93% | 203 |
| abstract_inverted_index.96.29% | 175 |
| abstract_inverted_index.96.97% | 205 |
| abstract_inverted_index.Common | 106 |
| abstract_inverted_index.Filter | 104 |
| abstract_inverted_index.bypass | 8 |
| abstract_inverted_index.fusion | 156 |
| abstract_inverted_index.intent | 91 |
| abstract_inverted_index.kernel | 139 |
| abstract_inverted_index.linear | 138 |
| abstract_inverted_index.mental | 98 |
| abstract_inverted_index.paper, | 59 |
| abstract_inverted_index.series | 122 |
| abstract_inverted_index.severe | 37 |
| abstract_inverted_index.single | 55 |
| abstract_inverted_index.speech | 40 |
| abstract_inverted_index.vector | 141 |
| abstract_inverted_index.versus | 182, 187, 193, 210, 213, 217 |
| abstract_inverted_index.(FBCSP) | 109 |
| abstract_inverted_index.(fTCD). | 77 |
| abstract_inverted_index.94.53%, | 172 |
| abstract_inverted_index.95.27%, | 202 |
| abstract_inverted_index.Average | 169 |
| abstract_inverted_index.Doppler | 75 |
| abstract_inverted_index.Pattern | 108 |
| abstract_inverted_index.Several | 120 |
| abstract_inverted_index.Spatial | 107 |
| abstract_inverted_index.Support | 140 |
| abstract_inverted_index.Whereas | 198 |
| abstract_inverted_index.average | 199 |
| abstract_inverted_index.control | 10 |
| abstract_inverted_index.enhance | 49 |
| abstract_inverted_index.exploit | 4 |
| abstract_inverted_index.extract | 114 |
| abstract_inverted_index.feature | 147 |
| abstract_inverted_index.imagery | 94 |
| abstract_inverted_index.improve | 28, 63 |
| abstract_inverted_index.quality | 30 |
| abstract_inverted_index.results | 221 |
| abstract_inverted_index.systems | 25 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Bayesian | 155 |
| abstract_inverted_index.Wilcoxon | 133 |
| abstract_inverted_index.achieved | 177, 207 |
| abstract_inverted_index.activity | 6 |
| abstract_inverted_index.analysis | 230 |
| abstract_inverted_index.approach | 157 |
| abstract_inverted_index.envelope | 128 |
| abstract_inverted_index.features | 115, 123 |
| abstract_inverted_index.included | 80 |
| abstract_inverted_index.machines | 142 |
| abstract_inverted_index.patients | 34 |
| abstract_inverted_index.proposed | 229 |
| abstract_inverted_index.recently | 47 |
| abstract_inverted_index.rotation | 99 |
| abstract_inverted_index.signals. | 132 |
| abstract_inverted_index.utilized | 84 |
| abstract_inverted_index.(MR)/word | 100 |
| abstract_inverted_index.EEG-fNRIS | 235 |
| abstract_inverted_index.algorithm | 110 |
| abstract_inverted_index.baseline, | 183, 188, 211, 214 |
| abstract_inverted_index.combining | 69 |
| abstract_inverted_index.different | 86 |
| abstract_inverted_index.extracted | 125 |
| abstract_inverted_index.including | 92 |
| abstract_inverted_index.modality. | 56 |
| abstract_inverted_index.paradigms | 87 |
| abstract_inverted_index.providing | 15 |
| abstract_inverted_index.selection | 148 |
| abstract_inverted_index.suffering | 35 |
| abstract_inverted_index.utilizing | 54 |
| abstract_inverted_index.Multimodal | 42 |
| abstract_inverted_index.accuracies | 170, 200 |
| abstract_inverted_index.flickering | 97 |
| abstract_inverted_index.functional | 73 |
| abstract_inverted_index.generation | 101 |
| abstract_inverted_index.interfaces | 2 |
| abstract_inverted_index.introduced | 46 |
| abstract_inverted_index.multimodal | 67, 234 |
| abstract_inverted_index.paradigms. | 103 |
| abstract_inverted_index.techniques | 231 |
| abstract_inverted_index.ultrasound | 76 |
| abstract_inverted_index.alternative | 16 |
| abstract_inverted_index.comparison. | 238 |
| abstract_inverted_index.impairment. | 41 |
| abstract_inverted_index.information | 163 |
| abstract_inverted_index.modalities. | 168 |
| abstract_inverted_index.performance | 51, 65 |
| abstract_inverted_index.surrounding | 22 |
| abstract_inverted_index.environment. | 23 |
| abstract_inverted_index.outperformed | 232 |
| abstract_inverted_index.transcranial | 74 |
| abstract_inverted_index.Additionally, | 152 |
| abstract_inverted_index.communication | 19 |
| abstract_inverted_index.neuromuscular | 9 |
| abstract_inverted_index.probabilistic | 154 |
| abstract_inverted_index.respectively. | 151, 197, 219 |
| abstract_inverted_index.significantly | 27 |
| abstract_inverted_index.Brain-computer | 1 |
| abstract_inverted_index.classification | 150 |
| abstract_inverted_index.Electroencephalography | 70 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.19545417 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |