Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet
Abstract In recent studies, machine learning and deep learning strategies have been explored in many EEG-based application for best performance. More specifically, convolutional neural networks (CNNs) have demonstrated incredible capacity in electroencephalograph (EEG)-evoked emotion classification...
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| Autor*in: |
Kumari, Nandini [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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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - London : Springer, 1993, 34(2022), 16 vom: 09. Feb., Seite 13291-13303 |
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| Übergeordnetes Werk: |
volume:34 ; year:2022 ; number:16 ; day:09 ; month:02 ; pages:13291-13303 |
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| DOI / URN: |
10.1007/s00521-022-06942-x |
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SPR047674229 |
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| 520 | |a Abstract In recent studies, machine learning and deep learning strategies have been explored in many EEG-based application for best performance. More specifically, convolutional neural networks (CNNs) have demonstrated incredible capacity in electroencephalograph (EEG)-evoked emotion classification tasks. In preexisting case, CNN-based emotion classification techniques using EEG signals mostly involve a moderately intricate phase of feature extrication before any network model implementation. The CNNs are not able to well describe the natural interrelation among the various EEG channels, which basically provides essential data for the classification of different emotion states. In this paper, an efficacious and advanced version of CNN called Emotion-based Capsule Network (EmotionCapsNet) for multi-channel EEG-based emotion classification to achieve better classification accuracy is presented. EmotionCapsNet has been applied to the raw EEG signals as well as 2D image representation generated from EEG signals which can extricate descriptive and complex features from the EEG signals and decide the different emotional states. The proposed system is then compared with the other conventional machine learning and deep learning-based CNN model. Our strategy accomplishes an average accuracy of 77.50%, 78.44% and 79.38% for valence, arousal and dominance on the DEAP, 79.06%, 78.90% and 79.69% on AMIGOS and attains an average accuracy of 80.34%, 83.04% and 82.50% for valence, arousal and dominance on the DREAMER, respectively. These outcomes demonstrate that adapted strategy yields comparable precision on raw EEG signal and it also provides better classification results on spatiotemporal feature of EEG signal for emotion classification task. | ||
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| 700 | 1 | |a Anwar, Shamama |4 aut | |
| 700 | 1 | |a Bhattacharjee, Vandana |4 aut | |
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10.1007/s00521-022-06942-x doi (DE-627)SPR047674229 (SPR)s00521-022-06942-x-e DE-627 ger DE-627 rakwb eng Kumari, Nandini verfasserin (orcid)0000-0002-6997-4298 aut Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract In recent studies, machine learning and deep learning strategies have been explored in many EEG-based application for best performance. More specifically, convolutional neural networks (CNNs) have demonstrated incredible capacity in electroencephalograph (EEG)-evoked emotion classification tasks. In preexisting case, CNN-based emotion classification techniques using EEG signals mostly involve a moderately intricate phase of feature extrication before any network model implementation. The CNNs are not able to well describe the natural interrelation among the various EEG channels, which basically provides essential data for the classification of different emotion states. In this paper, an efficacious and advanced version of CNN called Emotion-based Capsule Network (EmotionCapsNet) for multi-channel EEG-based emotion classification to achieve better classification accuracy is presented. EmotionCapsNet has been applied to the raw EEG signals as well as 2D image representation generated from EEG signals which can extricate descriptive and complex features from the EEG signals and decide the different emotional states. The proposed system is then compared with the other conventional machine learning and deep learning-based CNN model. Our strategy accomplishes an average accuracy of 77.50%, 78.44% and 79.38% for valence, arousal and dominance on the DEAP, 79.06%, 78.90% and 79.69% on AMIGOS and attains an average accuracy of 80.34%, 83.04% and 82.50% for valence, arousal and dominance on the DREAMER, respectively. These outcomes demonstrate that adapted strategy yields comparable precision on raw EEG signal and it also provides better classification results on spatiotemporal feature of EEG signal for emotion classification task. Time series data (dpeaa)DE-He213 Spatiotemporal (dpeaa)DE-He213 Spectrogram (dpeaa)DE-He213 Electroencephalograph (EEG) (dpeaa)DE-He213 EmotionCapsNet (dpeaa)DE-He213 Anwar, Shamama aut Bhattacharjee, Vandana aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 16 vom: 09. Feb., Seite 13291-13303 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:16 day:09 month:02 pages:13291-13303 https://dx.doi.org/10.1007/s00521-022-06942-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 16 09 02 13291-13303 |
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10.1007/s00521-022-06942-x doi (DE-627)SPR047674229 (SPR)s00521-022-06942-x-e DE-627 ger DE-627 rakwb eng Kumari, Nandini verfasserin (orcid)0000-0002-6997-4298 aut Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract In recent studies, machine learning and deep learning strategies have been explored in many EEG-based application for best performance. More specifically, convolutional neural networks (CNNs) have demonstrated incredible capacity in electroencephalograph (EEG)-evoked emotion classification tasks. In preexisting case, CNN-based emotion classification techniques using EEG signals mostly involve a moderately intricate phase of feature extrication before any network model implementation. The CNNs are not able to well describe the natural interrelation among the various EEG channels, which basically provides essential data for the classification of different emotion states. In this paper, an efficacious and advanced version of CNN called Emotion-based Capsule Network (EmotionCapsNet) for multi-channel EEG-based emotion classification to achieve better classification accuracy is presented. EmotionCapsNet has been applied to the raw EEG signals as well as 2D image representation generated from EEG signals which can extricate descriptive and complex features from the EEG signals and decide the different emotional states. The proposed system is then compared with the other conventional machine learning and deep learning-based CNN model. Our strategy accomplishes an average accuracy of 77.50%, 78.44% and 79.38% for valence, arousal and dominance on the DEAP, 79.06%, 78.90% and 79.69% on AMIGOS and attains an average accuracy of 80.34%, 83.04% and 82.50% for valence, arousal and dominance on the DREAMER, respectively. These outcomes demonstrate that adapted strategy yields comparable precision on raw EEG signal and it also provides better classification results on spatiotemporal feature of EEG signal for emotion classification task. Time series data (dpeaa)DE-He213 Spatiotemporal (dpeaa)DE-He213 Spectrogram (dpeaa)DE-He213 Electroencephalograph (EEG) (dpeaa)DE-He213 EmotionCapsNet (dpeaa)DE-He213 Anwar, Shamama aut Bhattacharjee, Vandana aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 16 vom: 09. Feb., Seite 13291-13303 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:16 day:09 month:02 pages:13291-13303 https://dx.doi.org/10.1007/s00521-022-06942-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 16 09 02 13291-13303 |
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10.1007/s00521-022-06942-x doi (DE-627)SPR047674229 (SPR)s00521-022-06942-x-e DE-627 ger DE-627 rakwb eng Kumari, Nandini verfasserin (orcid)0000-0002-6997-4298 aut Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract In recent studies, machine learning and deep learning strategies have been explored in many EEG-based application for best performance. More specifically, convolutional neural networks (CNNs) have demonstrated incredible capacity in electroencephalograph (EEG)-evoked emotion classification tasks. In preexisting case, CNN-based emotion classification techniques using EEG signals mostly involve a moderately intricate phase of feature extrication before any network model implementation. The CNNs are not able to well describe the natural interrelation among the various EEG channels, which basically provides essential data for the classification of different emotion states. In this paper, an efficacious and advanced version of CNN called Emotion-based Capsule Network (EmotionCapsNet) for multi-channel EEG-based emotion classification to achieve better classification accuracy is presented. EmotionCapsNet has been applied to the raw EEG signals as well as 2D image representation generated from EEG signals which can extricate descriptive and complex features from the EEG signals and decide the different emotional states. The proposed system is then compared with the other conventional machine learning and deep learning-based CNN model. Our strategy accomplishes an average accuracy of 77.50%, 78.44% and 79.38% for valence, arousal and dominance on the DEAP, 79.06%, 78.90% and 79.69% on AMIGOS and attains an average accuracy of 80.34%, 83.04% and 82.50% for valence, arousal and dominance on the DREAMER, respectively. These outcomes demonstrate that adapted strategy yields comparable precision on raw EEG signal and it also provides better classification results on spatiotemporal feature of EEG signal for emotion classification task. Time series data (dpeaa)DE-He213 Spatiotemporal (dpeaa)DE-He213 Spectrogram (dpeaa)DE-He213 Electroencephalograph (EEG) (dpeaa)DE-He213 EmotionCapsNet (dpeaa)DE-He213 Anwar, Shamama aut Bhattacharjee, Vandana aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 16 vom: 09. Feb., Seite 13291-13303 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:16 day:09 month:02 pages:13291-13303 https://dx.doi.org/10.1007/s00521-022-06942-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 16 09 02 13291-13303 |
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10.1007/s00521-022-06942-x doi (DE-627)SPR047674229 (SPR)s00521-022-06942-x-e DE-627 ger DE-627 rakwb eng Kumari, Nandini verfasserin (orcid)0000-0002-6997-4298 aut Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract In recent studies, machine learning and deep learning strategies have been explored in many EEG-based application for best performance. More specifically, convolutional neural networks (CNNs) have demonstrated incredible capacity in electroencephalograph (EEG)-evoked emotion classification tasks. In preexisting case, CNN-based emotion classification techniques using EEG signals mostly involve a moderately intricate phase of feature extrication before any network model implementation. The CNNs are not able to well describe the natural interrelation among the various EEG channels, which basically provides essential data for the classification of different emotion states. In this paper, an efficacious and advanced version of CNN called Emotion-based Capsule Network (EmotionCapsNet) for multi-channel EEG-based emotion classification to achieve better classification accuracy is presented. EmotionCapsNet has been applied to the raw EEG signals as well as 2D image representation generated from EEG signals which can extricate descriptive and complex features from the EEG signals and decide the different emotional states. The proposed system is then compared with the other conventional machine learning and deep learning-based CNN model. Our strategy accomplishes an average accuracy of 77.50%, 78.44% and 79.38% for valence, arousal and dominance on the DEAP, 79.06%, 78.90% and 79.69% on AMIGOS and attains an average accuracy of 80.34%, 83.04% and 82.50% for valence, arousal and dominance on the DREAMER, respectively. These outcomes demonstrate that adapted strategy yields comparable precision on raw EEG signal and it also provides better classification results on spatiotemporal feature of EEG signal for emotion classification task. Time series data (dpeaa)DE-He213 Spatiotemporal (dpeaa)DE-He213 Spectrogram (dpeaa)DE-He213 Electroencephalograph (EEG) (dpeaa)DE-He213 EmotionCapsNet (dpeaa)DE-He213 Anwar, Shamama aut Bhattacharjee, Vandana aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 16 vom: 09. Feb., Seite 13291-13303 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:16 day:09 month:02 pages:13291-13303 https://dx.doi.org/10.1007/s00521-022-06942-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 16 09 02 13291-13303 |
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10.1007/s00521-022-06942-x doi (DE-627)SPR047674229 (SPR)s00521-022-06942-x-e DE-627 ger DE-627 rakwb eng Kumari, Nandini verfasserin (orcid)0000-0002-6997-4298 aut Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract In recent studies, machine learning and deep learning strategies have been explored in many EEG-based application for best performance. More specifically, convolutional neural networks (CNNs) have demonstrated incredible capacity in electroencephalograph (EEG)-evoked emotion classification tasks. In preexisting case, CNN-based emotion classification techniques using EEG signals mostly involve a moderately intricate phase of feature extrication before any network model implementation. The CNNs are not able to well describe the natural interrelation among the various EEG channels, which basically provides essential data for the classification of different emotion states. In this paper, an efficacious and advanced version of CNN called Emotion-based Capsule Network (EmotionCapsNet) for multi-channel EEG-based emotion classification to achieve better classification accuracy is presented. EmotionCapsNet has been applied to the raw EEG signals as well as 2D image representation generated from EEG signals which can extricate descriptive and complex features from the EEG signals and decide the different emotional states. The proposed system is then compared with the other conventional machine learning and deep learning-based CNN model. Our strategy accomplishes an average accuracy of 77.50%, 78.44% and 79.38% for valence, arousal and dominance on the DEAP, 79.06%, 78.90% and 79.69% on AMIGOS and attains an average accuracy of 80.34%, 83.04% and 82.50% for valence, arousal and dominance on the DREAMER, respectively. These outcomes demonstrate that adapted strategy yields comparable precision on raw EEG signal and it also provides better classification results on spatiotemporal feature of EEG signal for emotion classification task. Time series data (dpeaa)DE-He213 Spatiotemporal (dpeaa)DE-He213 Spectrogram (dpeaa)DE-He213 Electroencephalograph (EEG) (dpeaa)DE-He213 EmotionCapsNet (dpeaa)DE-He213 Anwar, Shamama aut Bhattacharjee, Vandana aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 16 vom: 09. Feb., Seite 13291-13303 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:16 day:09 month:02 pages:13291-13303 https://dx.doi.org/10.1007/s00521-022-06942-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 16 09 02 13291-13303 |
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More specifically, convolutional neural networks (CNNs) have demonstrated incredible capacity in electroencephalograph (EEG)-evoked emotion classification tasks. In preexisting case, CNN-based emotion classification techniques using EEG signals mostly involve a moderately intricate phase of feature extrication before any network model implementation. The CNNs are not able to well describe the natural interrelation among the various EEG channels, which basically provides essential data for the classification of different emotion states. In this paper, an efficacious and advanced version of CNN called Emotion-based Capsule Network (EmotionCapsNet) for multi-channel EEG-based emotion classification to achieve better classification accuracy is presented. EmotionCapsNet has been applied to the raw EEG signals as well as 2D image representation generated from EEG signals which can extricate descriptive and complex features from the EEG signals and decide the different emotional states. The proposed system is then compared with the other conventional machine learning and deep learning-based CNN model. Our strategy accomplishes an average accuracy of 77.50%, 78.44% and 79.38% for valence, arousal and dominance on the DEAP, 79.06%, 78.90% and 79.69% on AMIGOS and attains an average accuracy of 80.34%, 83.04% and 82.50% for valence, arousal and dominance on the DREAMER, respectively. 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Kumari, Nandini |
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Kumari, Nandini misc Time series data misc Spatiotemporal misc Spectrogram misc Electroencephalograph (EEG) misc EmotionCapsNet Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet |
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Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet Time series data (dpeaa)DE-He213 Spatiotemporal (dpeaa)DE-He213 Spectrogram (dpeaa)DE-He213 Electroencephalograph (EEG) (dpeaa)DE-He213 EmotionCapsNet (dpeaa)DE-He213 |
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Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet |
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time series-dependent feature of eeg signals for improved visually evoked emotion classification using emotioncapsnet |
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Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet |
| abstract |
Abstract In recent studies, machine learning and deep learning strategies have been explored in many EEG-based application for best performance. More specifically, convolutional neural networks (CNNs) have demonstrated incredible capacity in electroencephalograph (EEG)-evoked emotion classification tasks. In preexisting case, CNN-based emotion classification techniques using EEG signals mostly involve a moderately intricate phase of feature extrication before any network model implementation. The CNNs are not able to well describe the natural interrelation among the various EEG channels, which basically provides essential data for the classification of different emotion states. In this paper, an efficacious and advanced version of CNN called Emotion-based Capsule Network (EmotionCapsNet) for multi-channel EEG-based emotion classification to achieve better classification accuracy is presented. EmotionCapsNet has been applied to the raw EEG signals as well as 2D image representation generated from EEG signals which can extricate descriptive and complex features from the EEG signals and decide the different emotional states. The proposed system is then compared with the other conventional machine learning and deep learning-based CNN model. Our strategy accomplishes an average accuracy of 77.50%, 78.44% and 79.38% for valence, arousal and dominance on the DEAP, 79.06%, 78.90% and 79.69% on AMIGOS and attains an average accuracy of 80.34%, 83.04% and 82.50% for valence, arousal and dominance on the DREAMER, respectively. These outcomes demonstrate that adapted strategy yields comparable precision on raw EEG signal and it also provides better classification results on spatiotemporal feature of EEG signal for emotion classification task. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
| abstractGer |
Abstract In recent studies, machine learning and deep learning strategies have been explored in many EEG-based application for best performance. More specifically, convolutional neural networks (CNNs) have demonstrated incredible capacity in electroencephalograph (EEG)-evoked emotion classification tasks. In preexisting case, CNN-based emotion classification techniques using EEG signals mostly involve a moderately intricate phase of feature extrication before any network model implementation. The CNNs are not able to well describe the natural interrelation among the various EEG channels, which basically provides essential data for the classification of different emotion states. In this paper, an efficacious and advanced version of CNN called Emotion-based Capsule Network (EmotionCapsNet) for multi-channel EEG-based emotion classification to achieve better classification accuracy is presented. EmotionCapsNet has been applied to the raw EEG signals as well as 2D image representation generated from EEG signals which can extricate descriptive and complex features from the EEG signals and decide the different emotional states. The proposed system is then compared with the other conventional machine learning and deep learning-based CNN model. Our strategy accomplishes an average accuracy of 77.50%, 78.44% and 79.38% for valence, arousal and dominance on the DEAP, 79.06%, 78.90% and 79.69% on AMIGOS and attains an average accuracy of 80.34%, 83.04% and 82.50% for valence, arousal and dominance on the DREAMER, respectively. These outcomes demonstrate that adapted strategy yields comparable precision on raw EEG signal and it also provides better classification results on spatiotemporal feature of EEG signal for emotion classification task. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract In recent studies, machine learning and deep learning strategies have been explored in many EEG-based application for best performance. More specifically, convolutional neural networks (CNNs) have demonstrated incredible capacity in electroencephalograph (EEG)-evoked emotion classification tasks. In preexisting case, CNN-based emotion classification techniques using EEG signals mostly involve a moderately intricate phase of feature extrication before any network model implementation. The CNNs are not able to well describe the natural interrelation among the various EEG channels, which basically provides essential data for the classification of different emotion states. In this paper, an efficacious and advanced version of CNN called Emotion-based Capsule Network (EmotionCapsNet) for multi-channel EEG-based emotion classification to achieve better classification accuracy is presented. EmotionCapsNet has been applied to the raw EEG signals as well as 2D image representation generated from EEG signals which can extricate descriptive and complex features from the EEG signals and decide the different emotional states. The proposed system is then compared with the other conventional machine learning and deep learning-based CNN model. Our strategy accomplishes an average accuracy of 77.50%, 78.44% and 79.38% for valence, arousal and dominance on the DEAP, 79.06%, 78.90% and 79.69% on AMIGOS and attains an average accuracy of 80.34%, 83.04% and 82.50% for valence, arousal and dominance on the DREAMER, respectively. These outcomes demonstrate that adapted strategy yields comparable precision on raw EEG signal and it also provides better classification results on spatiotemporal feature of EEG signal for emotion classification task. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
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| title_short |
Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet |
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https://dx.doi.org/10.1007/s00521-022-06942-x |
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Anwar, Shamama Bhattacharjee, Vandana |
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| score |
7.399063 |