EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach
<p dir="ltr">Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. This paper presents an...
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2019
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513552181624832 |
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| author | Muhammad Adeel Asghar (6724982) |
| author2 | Muhammad Jamil Khan (20568881) Fawad (20278068) Yasar Amin (16864354) Muhammad Rizwan (536386) MuhibUr Rahman (18174361) Salman Badnava (16864356) Seyed Sajad Mirjavadi (20278071) |
| author2_role | author author author author author author author |
| author_facet | Muhammad Adeel Asghar (6724982) Muhammad Jamil Khan (20568881) Fawad (20278068) Yasar Amin (16864354) Muhammad Rizwan (536386) MuhibUr Rahman (18174361) Salman Badnava (16864356) Seyed Sajad Mirjavadi (20278071) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Adeel Asghar (6724982) Muhammad Jamil Khan (20568881) Fawad (20278068) Yasar Amin (16864354) Muhammad Rizwan (536386) MuhibUr Rahman (18174361) Salman Badnava (16864356) Seyed Sajad Mirjavadi (20278071) |
| dc.date.none.fl_str_mv | 2019-11-28T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/s19235218 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/EEG-Based_Multi-Modal_Emotion_Recognition_using_Bag_of_Deep_Features_An_Optimal_Feature_Selection_Approach/28218686 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Information and computing sciences Machine learning emotion recognition brain computer interface bag of deep features continuous wavelet transform |
| dc.title.none.fl_str_mv | EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. This paper presents an advanced signal processing method using the deep neural network (DNN) for emotion recognition based on EEG signals. The spectral and temporal components of the raw EEG signal are first retained in the 2D Spectrogram before the extraction of features. The pre-trained AlexNet model is used to extract the raw features from the 2D Spectrogram for each channel. To reduce the feature dimensionality, spatial, and temporal based, bag of deep features (BoDF) model is proposed. A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. Lastly, the emotion of each subject is represented using the histogram of the vocabulary set collected from the raw-feature of a single channel. Features extracted from the proposed BoDF model have considerably smaller dimensions. The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. For optimal classification performance, we use a support vector machine (SVM) and k-nearest neighbor (k-NN) to classify the extracted features for the different emotional states of the two data sets. The BoDF model achieves 93.8% accuracy in the SEED data set and 77.4% accuracy in the DEAP data set, which is more accurate compared to other state-of-the-art methods of human emotion recognition.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s19235218" target="_blank">https://dx.doi.org/10.3390/s19235218</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_919aea14a1f605de9fb2024ac809a196 |
| identifier_str_mv | 10.3390/s19235218 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28218686 |
| publishDate | 2019 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection ApproachMuhammad Adeel Asghar (6724982)Muhammad Jamil Khan (20568881)Fawad (20278068)Yasar Amin (16864354)Muhammad Rizwan (536386)MuhibUr Rahman (18174361)Salman Badnava (16864356)Seyed Sajad Mirjavadi (20278071)Health sciencesHealth services and systemsInformation and computing sciencesMachine learningemotion recognitionbrain computer interfacebag of deep featurescontinuous wavelet transform<p dir="ltr">Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. This paper presents an advanced signal processing method using the deep neural network (DNN) for emotion recognition based on EEG signals. The spectral and temporal components of the raw EEG signal are first retained in the 2D Spectrogram before the extraction of features. The pre-trained AlexNet model is used to extract the raw features from the 2D Spectrogram for each channel. To reduce the feature dimensionality, spatial, and temporal based, bag of deep features (BoDF) model is proposed. A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. Lastly, the emotion of each subject is represented using the histogram of the vocabulary set collected from the raw-feature of a single channel. Features extracted from the proposed BoDF model have considerably smaller dimensions. The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. For optimal classification performance, we use a support vector machine (SVM) and k-nearest neighbor (k-NN) to classify the extracted features for the different emotional states of the two data sets. The BoDF model achieves 93.8% accuracy in the SEED data set and 77.4% accuracy in the DEAP data set, which is more accurate compared to other state-of-the-art methods of human emotion recognition.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s19235218" target="_blank">https://dx.doi.org/10.3390/s19235218</a></p>2019-11-28T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/s19235218https://figshare.com/articles/journal_contribution/EEG-Based_Multi-Modal_Emotion_Recognition_using_Bag_of_Deep_Features_An_Optimal_Feature_Selection_Approach/28218686CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/282186862019-11-28T09:00:00Z |
| spellingShingle | EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach Muhammad Adeel Asghar (6724982) Health sciences Health services and systems Information and computing sciences Machine learning emotion recognition brain computer interface bag of deep features continuous wavelet transform |
| status_str | publishedVersion |
| title | EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach |
| title_full | EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach |
| title_fullStr | EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach |
| title_full_unstemmed | EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach |
| title_short | EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach |
| title_sort | EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach |
| topic | Health sciences Health services and systems Information and computing sciences Machine learning emotion recognition brain computer interface bag of deep features continuous wavelet transform |