Overall optimization of CSP based on ensemble learning for motor imagery EEG decoding
• A new algorithm framework based on ensemble learning is proposed for the overall optimization of CSP. • A new temporal-spatial-frequency feature joint optimization method is proposed. • A new method of generating feature diversity is proposed. • A large number of data sets are used to verify the e...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Shaorong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood - Yan, Yinkun ELSEVIER, 2017, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:77 ; year:2022 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.bspc.2022.103825 |
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ELV058182195 |
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| 520 | |a • A new algorithm framework based on ensemble learning is proposed for the overall optimization of CSP. • A new temporal-spatial-frequency feature joint optimization method is proposed. • A new method of generating feature diversity is proposed. • A large number of data sets are used to verify the effectiveness, universality, and robustness of the proposed method. | ||
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• A new algorithm framework based on ensemble learning is proposed for the overall optimization of CSP. • A new temporal-spatial-frequency feature joint optimization method is proposed. • A new method of generating feature diversity is proposed. • A large number of data sets are used to verify the effectiveness, universality, and robustness of the proposed method. |
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• A new algorithm framework based on ensemble learning is proposed for the overall optimization of CSP. • A new temporal-spatial-frequency feature joint optimization method is proposed. • A new method of generating feature diversity is proposed. • A large number of data sets are used to verify the effectiveness, universality, and robustness of the proposed method. |
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• A new algorithm framework based on ensemble learning is proposed for the overall optimization of CSP. • A new temporal-spatial-frequency feature joint optimization method is proposed. • A new method of generating feature diversity is proposed. • A large number of data sets are used to verify the effectiveness, universality, and robustness of the proposed method. |
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