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dc.contributor.authorHancer, Emrah
dc.contributor.authorSubasi, Abdulhamit
dc.date.accessioned2023-03-12T12:57:26Z
dc.date.available2023-03-12T12:57:26Z
dc.date.issued2022-11-02
dc.identifier.citationEmrah Hancer & Abdulhamit Subasi (2022) EEG-based emotion recognition using dual tree complex wavelet transform and random subspace ensemble classifier, Computer Methods in Biomechanics and Biomedical Engineering, DOI: 10.1080/10255842.2022.2143714en_US
dc.identifier.doihttps://doi.org/10.1080/10255842.2022.2143714en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/579
dc.description.abstractEmotions are strongly admitted as a main source to establish meaningful interactions between humans and computers. Thanks to the advancements in electroencephalography (EEG), especially in the usage of portable and cheap wearable EEG devices, the demand for identifying emotions has extremely increased. However, the overall scientific knowledge and works concerning EEG-based emotion recognition is still limited. To cover this issue, we introduce an EEG-based emotion recognition framework in this study. The proposed framework involves the following stages: preprocessing, feature extraction, feature selection and classification. For the preprocessing stage, multi scale principle component analysis and sysmlets-4 filter are used. A version of discrete wavelet transform (DWT), namely dual tree complex wavelet transform (DTCWT) is utilized for the feature extraction stage. To reduce the feature dimension size, a variety of statistical criteria are employed. For the final stage, we adopt ensemble classifiers due to their promising performance in classification problems. The proposed framework achieves nearly 96.8% accuracy by using random subspace ensemble classifier. It can therefore be resulted that the proposed EEG-based framework performs well in terms of identifying emotions.en_US
dc.publisherTaylor & Francisen_US
dc.subjectEEGen_US
dc.subjectEmotion Recognitionen_US
dc.subjectDual Tree Complex Wavelet Transform (DTCWT)en_US
dc.subjectEnsemble Learningen_US
dc.titleEEG-based emotion recognition using dual tree complex wavelet transform and random subspace ensemble classifieren_US
dc.source.journalComputer Methods in Biomechanics and Biomedical Engineeringen_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSAHEALTHen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorHancer, Emrah


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