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• W4308769917 abstract "Emotions 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." @default.
• W4308769917 created "2022-11-15" @default.
• W4308769917 creator A5043777091 @default.
• W4308769917 creator A5076009908 @default.
• W4308769917 date "2022-11-11" @default.
• W4308769917 modified "2023-09-26" @default.
• W4308769917 title "EEG-based emotion recognition using dual tree complex wavelet transform and random subspace ensemble classifier" @default.
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• W4308769917 doi "https://doi.org/10.1080/10255842.2022.2143714" @default.
• W4308769917 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36367337" @default.
• W4308769917 hasPublicationYear "2022" @default.
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