A New PLV-Spatial Filtering to Improve the Classification Performance in BCI Systems
Objective: The performance of an EEG-based brain-computer interface (BCI) system is highly dependent on signal preprocessing. This manuscript presents a filtering method to improve the feature classification algorithms typically used in BCI. Methods: A graph Laplacian quadratic form using the Phase...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9853618/ |
| _version_ | 1797805152535576576 |
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| author | K. Martin-Chinea J. F. Gomez-Gonzalez L. Acosta |
| author_facet | K. Martin-Chinea J. F. Gomez-Gonzalez L. Acosta |
| author_sort | K. Martin-Chinea |
| collection | DOAJ |
| description | Objective: The performance of an EEG-based brain-computer interface (BCI) system is highly dependent on signal preprocessing. This manuscript presents a filtering method to improve the feature classification algorithms typically used in BCI. Methods: A graph Laplacian quadratic form using the Phase Locking Value (PLV) is applied to generate a new filtered signal in the preprocessing stage. Results: The accuracy of the classification algorithms improved significantly (up to 27.18% in the BCI Competition IV dataset, and up to 42.56% with records made with an Emotiv EPOC+). In addition, the proposed filtering algorithm has similar or better results when compared with the Filter Bank Common Spatial Pattern (FBCSP), which has disadvantages in a multiclass classification. Conclusion: This paper shows how our PLV-based filtering between EEG channels could improve the performance of a BCI. |
| first_indexed | 2024-03-13T05:46:53Z |
| format | Article |
| id | doaj.art-554aa0263fa84880b0f59b66250939ff |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:46:53Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-554aa0263fa84880b0f59b66250939ff2023-06-13T20:08:03ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102022-01-01302275228210.1109/TNSRE.2022.31980219853618A New PLV-Spatial Filtering to Improve the Classification Performance in BCI SystemsK. Martin-Chinea0https://orcid.org/0000-0003-4686-1183J. F. Gomez-Gonzalez1https://orcid.org/0000-0001-7737-2249L. Acosta2Department of Industrial Engineering, University of La Laguna, San Cristóbal de La Laguna, Tenerife, SpainDepartment of Industrial Engineering, University of La Laguna, San Cristóbal de La Laguna, Tenerife, SpainDepartment of Computer and Systems Engineering, University of La Laguna, San Cristóbal de La Laguna, Tenerife, SpainObjective: The performance of an EEG-based brain-computer interface (BCI) system is highly dependent on signal preprocessing. This manuscript presents a filtering method to improve the feature classification algorithms typically used in BCI. Methods: A graph Laplacian quadratic form using the Phase Locking Value (PLV) is applied to generate a new filtered signal in the preprocessing stage. Results: The accuracy of the classification algorithms improved significantly (up to 27.18% in the BCI Competition IV dataset, and up to 42.56% with records made with an Emotiv EPOC+). In addition, the proposed filtering algorithm has similar or better results when compared with the Filter Bank Common Spatial Pattern (FBCSP), which has disadvantages in a multiclass classification. Conclusion: This paper shows how our PLV-based filtering between EEG channels could improve the performance of a BCI.https://ieeexplore.ieee.org/document/9853618/Electroencephalographyphase locking valuebrain-computer interfacemachine learning |
| spellingShingle | K. Martin-Chinea J. F. Gomez-Gonzalez L. Acosta A New PLV-Spatial Filtering to Improve the Classification Performance in BCI Systems IEEE Transactions on Neural Systems and Rehabilitation Engineering Electroencephalography phase locking value brain-computer interface machine learning |
| title | A New PLV-Spatial Filtering to Improve the Classification Performance in BCI Systems |
| title_full | A New PLV-Spatial Filtering to Improve the Classification Performance in BCI Systems |
| title_fullStr | A New PLV-Spatial Filtering to Improve the Classification Performance in BCI Systems |
| title_full_unstemmed | A New PLV-Spatial Filtering to Improve the Classification Performance in BCI Systems |
| title_short | A New PLV-Spatial Filtering to Improve the Classification Performance in BCI Systems |
| title_sort | new plv spatial filtering to improve the classification performance in bci systems |
| topic | Electroencephalography phase locking value brain-computer interface machine learning |
| url | https://ieeexplore.ieee.org/document/9853618/ |
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