A spectral-ensemble deep random vector functional link network for passive brain–computer interface
<p dir="ltr">Randomized <u>neural networks</u> (RNNs) have shown outstanding performance in many different fields. The superiority of having fewer training parameters and closed-form solutions makes them popular in small datasets analysis. However, automatically decoding...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2023
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| الموضوعات: | |
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| _version_ | 1864513545941549056 |
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| author | Ruilin Li (5627456) |
| author2 | Ruobin Gao (16003195) Ponnuthurai N. Suganthan (17347024) Jian Cui (182110) Olga Sourina (17823674) Lipo Wang (17185534) |
| author2_role | author author author author author |
| author_facet | Ruilin Li (5627456) Ruobin Gao (16003195) Ponnuthurai N. Suganthan (17347024) Jian Cui (182110) Olga Sourina (17823674) Lipo Wang (17185534) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ruilin Li (5627456) Ruobin Gao (16003195) Ponnuthurai N. Suganthan (17347024) Jian Cui (182110) Olga Sourina (17823674) Lipo Wang (17185534) |
| dc.date.none.fl_str_mv | 2023-05-23T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.eswa.2023.120279 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_spectral-ensemble_deep_random_vector_functional_link_network_for_passive_brain_computer_interface/29445194 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Neurosciences Engineering Biomedical engineering Information and computing sciences Artificial intelligence Ensemble deep random vector functional link (edRVFL) Spectral-edRVFL (SedRVFL) Electroencephalogram (EEG) Feature-refining (FR) block Dynamic direct link (DDL) |
| dc.title.none.fl_str_mv | A spectral-ensemble deep random vector functional link network for passive brain–computer interface |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Randomized <u>neural networks</u> (RNNs) have shown outstanding performance in many different fields. The superiority of having fewer training parameters and closed-form solutions makes them popular in small datasets analysis. However, automatically decoding raw <u>electroencephalogram</u> (EEG) data using RNNs is still challenging in EEG-based passive brain–computer interface (pBCI) <u>classification tasks</u>. Models with the high-dimension input of EEG may suffer from overfitting and the intrinsic characteristics of non-stationary, high-level noises and subject variability could limit the generation of distinctive features in the hidden layers. To address these problems in EEG-based pBCI tasks, this work proposes a spectral-ensemble deep random vector functional link (SedRVFL) network that focuses on feature learning in the frequency domain. Specifically, an unsupervised feature-refining (FR) block is proposed to improve the low <u>feature learning</u> capability in RNNs. Moreover, a dynamic direct link (DDL) is performed to further complement the <u>frequency information</u>. The proposed model has been evaluated on a self-collected dataset as well as a public driving dataset. The cross-subject <u>classification results</u> obtained demonstrated its effectiveness. This work offers a new solution for EEG decoding, i.e., using optimized RNNs for decoding complex raw EEG data and boosting the classification performance of EEG-based pBCI tasks.</p><h2>Other Information</h2><p dir="ltr">Published in: Expert Systems with Applications<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.eswa.2023.120279" target="_blank">https://dx.doi.org/10.1016/j.eswa.2023.120279</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_7a631f74700121872591efb7ef505043 |
| identifier_str_mv | 10.1016/j.eswa.2023.120279 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29445194 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A spectral-ensemble deep random vector functional link network for passive brain–computer interfaceRuilin Li (5627456)Ruobin Gao (16003195)Ponnuthurai N. Suganthan (17347024)Jian Cui (182110)Olga Sourina (17823674)Lipo Wang (17185534)Biomedical and clinical sciencesNeurosciencesEngineeringBiomedical engineeringInformation and computing sciencesArtificial intelligenceEnsemble deep random vector functional link (edRVFL)Spectral-edRVFL (SedRVFL)Electroencephalogram (EEG)Feature-refining (FR) blockDynamic direct link (DDL)<p dir="ltr">Randomized <u>neural networks</u> (RNNs) have shown outstanding performance in many different fields. The superiority of having fewer training parameters and closed-form solutions makes them popular in small datasets analysis. However, automatically decoding raw <u>electroencephalogram</u> (EEG) data using RNNs is still challenging in EEG-based passive brain–computer interface (pBCI) <u>classification tasks</u>. Models with the high-dimension input of EEG may suffer from overfitting and the intrinsic characteristics of non-stationary, high-level noises and subject variability could limit the generation of distinctive features in the hidden layers. To address these problems in EEG-based pBCI tasks, this work proposes a spectral-ensemble deep random vector functional link (SedRVFL) network that focuses on feature learning in the frequency domain. Specifically, an unsupervised feature-refining (FR) block is proposed to improve the low <u>feature learning</u> capability in RNNs. Moreover, a dynamic direct link (DDL) is performed to further complement the <u>frequency information</u>. The proposed model has been evaluated on a self-collected dataset as well as a public driving dataset. The cross-subject <u>classification results</u> obtained demonstrated its effectiveness. This work offers a new solution for EEG decoding, i.e., using optimized RNNs for decoding complex raw EEG data and boosting the classification performance of EEG-based pBCI tasks.</p><h2>Other Information</h2><p dir="ltr">Published in: Expert Systems with Applications<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.eswa.2023.120279" target="_blank">https://dx.doi.org/10.1016/j.eswa.2023.120279</a></p>2023-05-23T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.eswa.2023.120279https://figshare.com/articles/journal_contribution/A_spectral-ensemble_deep_random_vector_functional_link_network_for_passive_brain_computer_interface/29445194CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/294451942023-05-23T15:00:00Z |
| spellingShingle | A spectral-ensemble deep random vector functional link network for passive brain–computer interface Ruilin Li (5627456) Biomedical and clinical sciences Neurosciences Engineering Biomedical engineering Information and computing sciences Artificial intelligence Ensemble deep random vector functional link (edRVFL) Spectral-edRVFL (SedRVFL) Electroencephalogram (EEG) Feature-refining (FR) block Dynamic direct link (DDL) |
| status_str | publishedVersion |
| title | A spectral-ensemble deep random vector functional link network for passive brain–computer interface |
| title_full | A spectral-ensemble deep random vector functional link network for passive brain–computer interface |
| title_fullStr | A spectral-ensemble deep random vector functional link network for passive brain–computer interface |
| title_full_unstemmed | A spectral-ensemble deep random vector functional link network for passive brain–computer interface |
| title_short | A spectral-ensemble deep random vector functional link network for passive brain–computer interface |
| title_sort | A spectral-ensemble deep random vector functional link network for passive brain–computer interface |
| topic | Biomedical and clinical sciences Neurosciences Engineering Biomedical engineering Information and computing sciences Artificial intelligence Ensemble deep random vector functional link (edRVFL) Spectral-edRVFL (SedRVFL) Electroencephalogram (EEG) Feature-refining (FR) block Dynamic direct link (DDL) |