Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals
Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approach...
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| Published in | Sensors (Basel, Switzerland) Vol. 19; no. 13; p. 2999 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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08.07.2019
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| Abstract | Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject’s influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject’s influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results. |
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| AbstractList | Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject’s influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject’s influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results. Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject's influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject's influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject's influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject's influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results. |
| Author | Roger, Sandra García-Pineda, Miguel Arevalillo-Herráez, Miguel Cobos, Maximo |
| AuthorAffiliation | Departament d’Informàtica, Universitat de València, Avda. de la Universidad, s/n, 46100-Burjasot, Spain |
| AuthorAffiliation_xml | – name: Departament d’Informàtica, Universitat de València, Avda. de la Universidad, s/n, 46100-Burjasot, Spain |
| Author_xml | – sequence: 1 givenname: Miguel orcidid: 0000-0002-0350-2079 surname: Arevalillo-Herráez fullname: Arevalillo-Herráez, Miguel – sequence: 2 givenname: Maximo orcidid: 0000-0001-7318-3192 surname: Cobos fullname: Cobos, Maximo – sequence: 3 givenname: Sandra orcidid: 0000-0003-4808-252X surname: Roger fullname: Roger, Sandra – sequence: 4 givenname: Miguel orcidid: 0000-0003-2590-6370 surname: García-Pineda fullname: García-Pineda, Miguel |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31288378$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.eswa.2018.03.011 10.1109/ICASSP.2013.6637858 10.3390/s18113886 10.1109/PRNI.2014.6858538 10.3390/s19112499 10.1016/j.jneumeth.2017.05.004 10.1109/ICCSP.2015.7322687 10.1109/TPAMI.2008.52 10.1136/bjsports-2012-091877 10.3390/s19050987 10.1109/TSP.2012.2187647 10.1109/FG.2013.6553809 10.1007/BF02686918 10.1155/2013/573734 10.1016/j.procs.2015.07.314 10.1016/j.knosys.2016.04.026 10.3390/s19092212 10.1109/TAFFC.2014.2339834 10.1109/T-AFFC.2011.25 10.3390/s19102302 10.5405/jmbe.710 10.1109/TITB.2009.2034649 10.1016/j.neucom.2013.06.046 10.1109/ICCI-CC.2014.6921443 10.1109/T-AFFC.2010.1 10.1007/978-3-319-19773-9 10.3390/e21050479 10.1016/j.neucom.2017.03.027 10.1109/TITB.2011.2157933 10.3390/sym11010115 10.3390/s19010006 10.1016/j.asoc.2016.11.002 10.1186/s12911-017-0562-x 10.3390/s18051372 10.1109/CW.2010.37 10.1016/j.eswa.2015.10.049 10.3390/s18072074 10.1155/2014/627892 10.1109/BIBE.2017.00-74 10.1007/978-3-642-39454-6 10.1016/j.neucom.2015.04.025 10.1260/2047-4970.4.2.165 10.3390/s19071631 10.1109/JBHI.2017.2688239 10.1109/T-AFFC.2011.15 10.1109/TBME.2010.2048568 10.1155/2013/618649 10.1037/0022-3514.72.2.435 10.1007/978-3-642-10439-8_6 10.1109/TNSRE.2012.2236576 10.1109/SMC.2014.6974415 10.1016/j.imavis.2012.06.016 10.1037/0022-3514.37.3.345 10.1016/j.knosys.2017.10.032 10.1145/3058060.3058068 10.3390/s19020219 10.1016/j.knosys.2017.06.024 10.3390/s19081863 10.3389/fnins.2018.00162 10.1109/TIFS.2016.2543524 10.1037/0022-3514.48.4.813 10.4236/jbise.2010.34054 10.1109/THMS.2017.2682115 |
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| Keywords | arousal detection valence detection data transformation normalization EEG |
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| References | ref_50 Murugappan (ref_44) 2011; 31 ref_13 ref_57 ref_55 Schuller (ref_38) 2013; 31 Murugappan (ref_52) 2010; 3 Li (ref_30) 2018; 12 Petrantonakis (ref_48) 2011; 15 Jirayucharoensak (ref_56) 2014; 2014 ref_17 Ramzan (ref_18) 2017; 244 Lin (ref_47) 2010; 57 Mumtaz (ref_11) 2016; 105 ref_15 ref_59 Soleymani (ref_34) 2012; 3 Hu (ref_16) 2018; 140 Bozhkov (ref_64) 2015; 53 Patidar (ref_10) 2017; 50 ref_23 ref_22 ref_66 ref_21 ref_20 Smith (ref_39) 1985; 48 ref_63 Prasad (ref_12) 2018; 103 ref_27 Yang (ref_14) 2017; 47 Wang (ref_51) 2013; 21 Armstrong (ref_62) 2015; 166 Wang (ref_28) 2014; 129 ref_32 ref_31 Mehrabian (ref_54) 1996; 14 Jatupaiboon (ref_65) 2013; 2013 Kim (ref_19) 2013; 2013 Hadjidimitriou (ref_29) 2015; 4 Hinton (ref_60) 2008; 9 ref_45 Russell (ref_53) 1979; 37 ref_42 Calvo (ref_36) 2010; 1 ref_40 Petrantonakis (ref_46) 2010; 14 ref_1 ref_3 ref_2 Gross (ref_26) 1997; 72 Aspinall (ref_58) 2015; 49 Petrantonakis (ref_41) 2012; 60 ref_49 ref_9 ref_8 Kia (ref_43) 2017; 285 Garcia (ref_67) 2008; 9 ref_5 Jenke (ref_24) 2014; 5 Koelstra (ref_33) 2012; 3 Katsigiannis (ref_35) 2017; 22 ref_4 Arnau (ref_7) 2017; 132 Campos (ref_25) 2016; 47 Jin (ref_61) 2016; 11 ref_6 Zeng (ref_37) 2009; 31 |
| References_xml | – volume: 103 start-page: 206 year: 2018 ident: ref_12 article-title: Sentiment analysis using EEG activities for suicidology publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2018.03.011 – ident: ref_57 doi: 10.1109/ICASSP.2013.6637858 – ident: ref_5 doi: 10.3390/s18113886 – ident: ref_42 doi: 10.1109/PRNI.2014.6858538 – ident: ref_13 doi: 10.3390/s19112499 – volume: 9 start-page: 2677 year: 2008 ident: ref_67 article-title: An Extension on Statistical Comparisons of Classifiers over Multiple Data Sets for all Pairwise Comparisons publication-title: J. Mach. Learn. Res. – volume: 285 start-page: 97 year: 2017 ident: ref_43 article-title: Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2017.05.004 – ident: ref_49 doi: 10.1109/ICCSP.2015.7322687 – volume: 9 start-page: 2579 year: 2008 ident: ref_60 article-title: Visualizing data using t-SNE publication-title: J. Mach. Learn. Res. – volume: 31 start-page: 39 year: 2009 ident: ref_37 article-title: A survey of affect recognition methods: Audio, visual, and spontaneous expressions publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2008.52 – volume: 49 start-page: 272 year: 2015 ident: ref_58 article-title: The urban brain: Analysing outdoor physical activity with mobile EEG publication-title: Br. J. Sports Med. doi: 10.1136/bjsports-2012-091877 – ident: ref_21 doi: 10.3390/s19050987 – volume: 60 start-page: 2604 year: 2012 ident: ref_41 article-title: Adaptive Emotional Information Retrieval From EEG Signals in the Time-Frequency Domain publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2012.2187647 – ident: ref_6 doi: 10.1109/FG.2013.6553809 – volume: 14 start-page: 261 year: 1996 ident: ref_54 article-title: Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in Temperament publication-title: Curr. Psychol. doi: 10.1007/BF02686918 – volume: 2013 start-page: 573734 year: 2013 ident: ref_19 article-title: A Review on the Computational Methods for Emotional State Estimation from the Human EEG publication-title: Comput. Math. Methods Med. doi: 10.1155/2013/573734 – volume: 53 start-page: 375 year: 2015 ident: ref_64 article-title: EEG-based subject independent affective computing models publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.07.314 – volume: 105 start-page: 48 year: 2016 ident: ref_11 article-title: Automatic diagnosis of alcohol use disorder using EEG features publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2016.04.026 – ident: ref_17 doi: 10.3390/s19092212 – volume: 5 start-page: 327 year: 2014 ident: ref_24 article-title: Feature Extraction and Selection for Emotion Recognition from EEG publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/TAFFC.2014.2339834 – volume: 3 start-page: 42 year: 2012 ident: ref_34 article-title: A Multimodal Database for Affect Recognition and Implicit Tagging publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/T-AFFC.2011.25 – ident: ref_22 doi: 10.3390/s19102302 – volume: 31 start-page: 45 year: 2011 ident: ref_44 article-title: Combining spatial filtering and wavelet transform for classifying human emotions using EEG Signals publication-title: J. Med. Biol. Eng. doi: 10.5405/jmbe.710 – volume: 14 start-page: 186 year: 2010 ident: ref_46 article-title: Emotion Recognition From EEG Using Higher Order Crossings publication-title: IEEE Trans. Inf. Technol. Biomed. doi: 10.1109/TITB.2009.2034649 – volume: 129 start-page: 94 year: 2014 ident: ref_28 article-title: Emotional state classification from EEG data using machine learning approach publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.06.046 – ident: ref_32 doi: 10.1109/ICCI-CC.2014.6921443 – volume: 1 start-page: 18 year: 2010 ident: ref_36 article-title: Affect detection: An interdisciplinary review of models, methods, and their applications publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/T-AFFC.2010.1 – ident: ref_31 doi: 10.1007/978-3-319-19773-9 – ident: ref_45 – ident: ref_3 doi: 10.3390/e21050479 – volume: 244 start-page: 81 year: 2017 ident: ref_18 article-title: Fusing highly dimensional energy and connectivity features to identify affective states from EEG signals publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.03.027 – volume: 15 start-page: 737 year: 2011 ident: ref_48 article-title: A novel emotion elicitation index using frontal brain asymmetry for enhanced EEG-based emotion recognition publication-title: IEEE Trans. Inf. Technol. Biomed. doi: 10.1109/TITB.2011.2157933 – ident: ref_1 doi: 10.3390/sym11010115 – ident: ref_15 doi: 10.3390/s19010006 – volume: 50 start-page: 71 year: 2017 ident: ref_10 article-title: An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2016.11.002 – ident: ref_20 – ident: ref_27 doi: 10.1186/s12911-017-0562-x – ident: ref_9 doi: 10.3390/s18051372 – ident: ref_59 doi: 10.1109/CW.2010.37 – volume: 47 start-page: 35 year: 2016 ident: ref_25 article-title: Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2015.10.049 – ident: ref_4 doi: 10.3390/s18072074 – volume: 2014 start-page: 627892 year: 2014 ident: ref_56 article-title: EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation publication-title: Sci. World J. doi: 10.1155/2014/627892 – ident: ref_66 doi: 10.1109/BIBE.2017.00-74 – ident: ref_40 doi: 10.1007/978-3-642-39454-6 – volume: 166 start-page: 59 year: 2015 ident: ref_62 article-title: Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.04.025 – volume: 4 start-page: 165 year: 2015 ident: ref_29 article-title: Towards a Practical Subject-Independent Affective State Recognition Based On Time-Domain EEG Feature Extraction publication-title: Int. J. Herit. Digit. Era doi: 10.1260/2047-4970.4.2.165 – ident: ref_23 doi: 10.3390/s19071631 – volume: 22 start-page: 98 year: 2017 ident: ref_35 article-title: DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2017.2688239 – volume: 3 start-page: 18 year: 2012 ident: ref_33 article-title: DEAP: A Database for Emotion Analysis using Physiological Signals publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/T-AFFC.2011.15 – volume: 57 start-page: 1798 year: 2010 ident: ref_47 article-title: EEG-Based Emotion Recognition in Music Listening publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2010.2048568 – volume: 2013 start-page: 618649 year: 2013 ident: ref_65 article-title: Real-time EEG-based happiness detection system publication-title: Sci. World J. doi: 10.1155/2013/618649 – volume: 72 start-page: 435 year: 1997 ident: ref_26 article-title: Revealing Feelings: Facets of Emotional Expressivity in Self-Reports, Peer Ratings, and Behavior publication-title: J. Pers. Soc. Psychol. doi: 10.1037/0022-3514.72.2.435 – ident: ref_50 doi: 10.1007/978-3-642-10439-8_6 – volume: 21 start-page: 225 year: 2013 ident: ref_51 article-title: Real-time mental arithmetic task recognition from EEG signals publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2012.2236576 – ident: ref_55 doi: 10.1109/SMC.2014.6974415 – volume: 31 start-page: 120 year: 2013 ident: ref_38 article-title: Categorical and dimensional affect analysis in continuous input: Current trends and future directions publication-title: Image Vis. Comput. doi: 10.1016/j.imavis.2012.06.016 – volume: 37 start-page: 345 year: 1979 ident: ref_53 article-title: Affective Space is Bipolar publication-title: J. Personal. Soc. Psychol. doi: 10.1037/0022-3514.37.3.345 – volume: 140 start-page: 134 year: 2018 ident: ref_16 article-title: An approach to EEG-based gender recognition using entropy measurement methods publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2017.10.032 – ident: ref_63 doi: 10.1145/3058060.3058068 – ident: ref_8 doi: 10.3390/s19020219 – volume: 132 start-page: 85 year: 2017 ident: ref_7 article-title: Adding sensor-free intention-based affective support to an Intelligent Tutoring System publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2017.06.024 – ident: ref_2 doi: 10.3390/s19081863 – volume: 12 start-page: 162 year: 2018 ident: ref_30 article-title: Exploring EEG Features in Cross-Subject Emotion Recognition publication-title: Front. Neurosci. doi: 10.3389/fnins.2018.00162 – volume: 11 start-page: 1618 year: 2016 ident: ref_61 article-title: CEREBRE: A novel method for very high accuracy event-related potential biometric identification publication-title: IEEE Trans. Inf. Forensics Secur. doi: 10.1109/TIFS.2016.2543524 – volume: 48 start-page: 813 year: 1985 ident: ref_39 article-title: Patterns of cognitive appraisal in emotion publication-title: J. Pers. Soc. Psychol. doi: 10.1037/0022-3514.48.4.813 – volume: 3 start-page: 390 year: 2010 ident: ref_52 article-title: Classification of human emotion from EEG using discrete wavelet transform publication-title: J. Biomed. Sci. Eng. doi: 10.4236/jbise.2010.34054 – volume: 47 start-page: 958 year: 2017 ident: ref_14 article-title: On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey publication-title: IEEE Trans. Hum. Mach. Syst. doi: 10.1109/THMS.2017.2682115 |
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| SubjectTerms | Algorithms Arousal - physiology arousal detection Artificial intelligence Biometric identification Biometrics Data Analysis data transformation Databases, Factual EEG Electroencephalography Electroencephalography - methods Emotions Emotions - physiology Fourier transforms Humans Information sources International conferences Machine learning Methods Models, Biological normalization Physiology Signal processing Signal Processing, Computer-Assisted Support Vector Machine valence detection Wavelet transforms |
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| Title | Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals |
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