Academic Journal
Cross-Subject EEG Emotion Recognition Using SSA-EMS Algorithm for Feature Extraction.
| Title: | Cross-Subject EEG Emotion Recognition Using SSA-EMS Algorithm for Feature Extraction. |
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| Authors: | Lu, Yuan, Chen, Jingying |
| Source: | Entropy; Sep2025, Vol. 27 Issue 9, p986, 16p |
| Subject Terms: | Emotion recognition, Feature extraction, Support vector machines, Algorithms, Electroencephalography, Neurosciences, Random forest algorithms |
| Abstract: | This study proposes a novel SSA-EMS framework that integrates Singular Spectrum Analysis (SSA) with Effect-Matched Spatial Filtering (EMS), combining the noise-reduction capability of SSA with the dynamic feature extraction advantages of EMS to optimize cross-subject EEG-based emotion feature extraction. Experiments were conducted using the SEED dataset under two evaluation paradigms: "cross-subject sample combination" and "subject-independent" assessment. Random Forest (RF) and SVM classifiers were employed to perform pairwise classification of three emotional states—positive, neutral, and negative. Results demonstrate that the SSA-EMS framework achieves RF classification accuracies exceeding 98% across the full frequency band, significantly outperforming single frequency bands. Notably, in the subject-independent evaluation, model accuracy remains above 96%, confirming the algorithm's strong cross-subject generalization capability. Experimental results validate that the SSA-EMS framework effectively captures dynamic neural differences associated with emotions. Nevertheless, limitations in binary classification and the potential for multimodal extension remain important directions for future research. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Cross-Subject EEG Emotion Recognition Using SSA-EMS Algorithm for Feature Extraction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lu%2C+Yuan%22">Lu, Yuan</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Jingying%22">Chen, Jingying</searchLink> – Name: TitleSource Label: Source Group: Src Data: Entropy; Sep2025, Vol. 27 Issue 9, p986, 16p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Emotion+recognition%22">Emotion recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Neurosciences%22">Neurosciences</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study proposes a novel SSA-EMS framework that integrates Singular Spectrum Analysis (SSA) with Effect-Matched Spatial Filtering (EMS), combining the noise-reduction capability of SSA with the dynamic feature extraction advantages of EMS to optimize cross-subject EEG-based emotion feature extraction. Experiments were conducted using the SEED dataset under two evaluation paradigms: "cross-subject sample combination" and "subject-independent" assessment. Random Forest (RF) and SVM classifiers were employed to perform pairwise classification of three emotional states—positive, neutral, and negative. Results demonstrate that the SSA-EMS framework achieves RF classification accuracies exceeding 98% across the full frequency band, significantly outperforming single frequency bands. Notably, in the subject-independent evaluation, model accuracy remains above 96%, confirming the algorithm's strong cross-subject generalization capability. Experimental results validate that the SSA-EMS framework effectively captures dynamic neural differences associated with emotions. Nevertheless, limitations in binary classification and the potential for multimodal extension remain important directions for future research. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Entropy is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/e27090986 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 986 Subjects: – SubjectFull: Emotion recognition Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Electroencephalography Type: general – SubjectFull: Neurosciences Type: general – SubjectFull: Random forest algorithms Type: general Titles: – TitleFull: Cross-Subject EEG Emotion Recognition Using SSA-EMS Algorithm for Feature Extraction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lu, Yuan – PersonEntity: Name: NameFull: Chen, Jingying IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10994300 Numbering: – Type: volume Value: 27 – Type: issue Value: 9 Titles: – TitleFull: Entropy Type: main |
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