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From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification

J. Wagner, Jonghwa Kim, E. Andre
2005 IEEE International Conference on Multimedia and Expo  
After calculating a sufficient amount of features from the raw signals, several feature selection/reduction methods are tested to extract a new feature set consisting of the most significant features for  ...  Little attention has been paid so far to physiological signals for emotion recognition compared to audio-visual emotion channels, such as facial expressions or speech.  ...  FEATURE EXTRACTION AND CLASSIFICATION Firstly, the raw signals were trimmed to a fixed length of two minutes.  ... 
doi:10.1109/icme.2005.1521579 dblp:conf/icmcs/WagnerKA05 fatcat:2fb26fboqngetlu74aedcsbyke

Seven Principles to Mine Flexible Behavior from Physiological Signals for Effective Emotion Recognition and Description in Affective Interactions

Rui Henriques, Ana Paiva
2014 Proceedings of the International Conference on Physiological Computing Systems  
In this work, we rely on empirical results to define seven principles for a robust mining of physiological signals to recognize and characterize affective states.  ...  However, traditional methods for signal analysis are not yet able to effectively deal with the differences of responses across individuals and with flexible sequential behavior.  ...  ACKNOWLEDGEMENTS This work was supported by Fundao para a Ciłncia e a Tecnologia under the project PEst-OE/EEI/LA0021/2013 and PhD grant SFRH/BD/ 75924/2011, and by the project EMOTE from the EU 7thFramework  ... 
doi:10.5220/0004666400750082 dblp:conf/phycs/HenriquesP14 fatcat:eoeuifybkffutm7xefbect2q6u

Machine Learning and End-to-end Deep Learning for Monitoring Driver Distractions from Physiological and Visual Signals

M. Gjoreski, M. Gams, M. Lustrek, P. Genc, J.-U. Garbas, T. Hassan
2020 IEEE Access  
This study compared data from physiological sensors (palm electrodermal activity (pEDA), heart rate and breathing rate) and visual sensors (eye tracking, pupil diameter, nasal EDA (nEDA), emotional activation  ...  The statistical tests showed that the most informative feature/modality for detecting driver distraction depends on the type of distraction, with emotional activation and AUs being the most promising.  ...  The feature selection can significantly influence the classification performance of the classical feature-based ML methods.  ... 
doi:10.1109/access.2020.2986810 fatcat:gp47clbjd5astd2zb5negkvuf4

An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations

Ana Serrano-Mamolar, Miguel Arevalillo-Herráez, Guillermo Chicote-Huete, Jesus G. Boticario
2021 Sensors  
Therefore, emotion recognition is becoming very relevant in educational scenarios, but there is no standard method for predicting students' affects.  ...  To tackle this problem, in this work, we analyse the feasibility of developing low-cost and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals.  ...  Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data sharing not applicable.  ... 
doi:10.3390/s21051777 pmid:33806438 fatcat:bsmoxmd2engw7gifq35qkudciu

MPED: A Multi-Modal Physiological Emotion Database for Discrete Emotion Recognition

Tengfei Song, Wenming Zheng, Cheng Lu, Yuan Zong, Xilei Zhang, Zhen Cui
2019 IEEE Access  
With three types of classification protocols, different feature extraction methods and classifiers (support vector machine and k-NearestNeighbor) were used to recognize the physiological responses of different  ...  To alleviate the influence of culture dependent elicitation materials and evoke desired human emotions, we specifically collect an emotion elicitation material database selected from more than 1500 video  ...  FEATURE EXTRACTION FROM PHYSIOLOGICAL SIGNALS After signals acquisition, a key process was feature extraction from these physiological signals.  ... 
doi:10.1109/access.2019.2891579 fatcat:okf54yq2bjbw5aqwyqq7ab2kdy

Emotion Detection from Brain and Audio Signal

Priyanka A. Wandile, Dr.Narendra Bawane, Mr.Pratik Hajare
2015 International Journal of Scientific Research and Management  
After extracting the feature signal is given to Neural Network for classification. In the past few days, many studies have been done on emotion recognition.  ...  In this system work EEG signals have been used to extract the feature, the feature is extracted by using wavelet transform such as Db6.  ...  Narendra Bawane and Mr. Pravin Hajare from S. B. Jain Institute of Technology Management & Research for technical discussion & processing support without whom this paper would never be completed.  ... 
doi:10.18535/ijsrm/v3i8.12 fatcat:xmppm3ftq5dwnishmm2g45xury

Multimodal Emotion Evaluation: A Physiological Model for Cost-Effective Emotion Classification

Gisela Pinto, João M. Carvalho, Filipa Barros, Sandra C. Soares, Armando J. Pinho, Susana Brás
2020 Sensors  
The method was divided in: (1) signal preprocessing; (2) feature extraction; (3) classification using random forest and neural networks.  ...  This physiological model of emotions has important research and clinical implications, by providing valuable information about the value and weight of physiological signals for emotional classification  ...  This physiological model of emotions has important research and clinical implications, by providing valuable information about the value and weight of physiological signals for emotional classification  ... 
doi:10.3390/s20123510 pmid:32575894 fatcat:lkzr4sm3angghkzulnebrn7tsm

Towards Emotional Interaction: Using Movies to Automatically Learn Users' Emotional States [chapter]

Eva Oliveira, Mitchel Benovoy, Nuno Ribeiro, Teresa Chambel
2011 Lecture Notes in Computer Science  
A novel pattern recognition system, based on discriminant analysis and support vector machine classifiers is trained using movies' scenes selected to induce emotions ranging from the positive to the negative  ...  In this paper we introduce an emotion recognition system and evaluate its accuracy by presenting the results of an experiment conducted with three physiologic sensors.  ...  different physiologic signals and different classification methods.  ... 
doi:10.1007/978-3-642-23774-4_15 fatcat:iiatxullsvdi3om635fszzy6xq

Decoding Emotional Experience through Physiological Signal Processing [article]

Maria S. Perez-Rosero, Behnaz Rezaei, Murat Akcakaya, Sarah Ostadabbas
2016 arXiv   pre-print
In order to fully exploit the information in each modality, we have provided an innovative classification approach for three specific physiological signals including Electromyogram (EMG), Blood Volume  ...  Furthermore, in order to avoid information redundancy and the resultant over-fitting, a feature reduction method is proposed based on a correlation analysis to optimize the number of features required  ...  The training and test sets were built up using all the extracted features from the three physiological signals.  ... 
arXiv:1606.00370v1 fatcat:3rdgut4nyncopo7pef4fiu4ofe

Speech emotion recognition in emotional feedback for Human-Robot Interaction

Javier G., David Sundgren, Rahim Rahmani, Aron Larsson, Antonio Moran, Isis Bonet
2015 International Journal of Advanced Research in Artificial Intelligence (IJARAI)  
The classification techniques used information from six audio files extracted from the eNTERFACE05 audiovisual emotion database.  ...  For robots to plan their actions autonomously and interact with people, recognizing human emotions is crucial.  ...  We also grateful to Antioquia School of Engineering "EIA" (Colombia) and Pontifical Catholic University (Perú) in a joint effort for collaborative research.  ... 
doi:10.14569/ijarai.2015.040204 fatcat:brgnmvxvx5f3vnnv4x7b3wy77a

Detecting Emotion from EEG Signals Using the Emotive Epoc Device [chapter]

Rafael Ramirez, Zacharias Vamvakousis
2012 Lecture Notes in Computer Science  
First, we extract features from the EEG signals in order to characterize states of mind in the arousal-valence 2D emotion model.  ...  Using these features we apply machine learning techniques to classify EEG signals into high/low arousal and positive/negative valence emotional states.  ...  Data Classification Feature Extraction As mentioned in the previous section, in EEG signals the alpha (8-12Hz) and beta (12-30Hz) bands are particular bands of interest in emotion research for both valence  ... 
doi:10.1007/978-3-642-35139-6_17 fatcat:ayysobvovzhw5ea5eraz4qs6iy

Emotion Recognition using Machine Learning and ECG signals [article]

Bo Sun, Zihuai Lin
2022 arXiv   pre-print
We use the Discrete Cosine Transform (DCT) to extract characteristics from the obtained data to increase the accuracy of emotion recognition.  ...  This study is about emotion recognition using ECG signals. Data for four emotions, namely happy, exciting, calm, and tense, is gathered. The raw data is then de-noised with a finite impulse filter.  ...  Compared with previous methods of emotion recognition, there are two benefits to using physiological signals on emotion detection.  ... 
arXiv:2203.08477v1 fatcat:faozeczrozbcpigmyf2iqgu6w4

An Integrated Approach to Emotion Recognition for Advanced Emotional Intelligence [chapter]

Panagiotis D. Bamidis, Christos A. Frantzidis, Evdokimos I. Konstantinidis, Andrej Luneski, Chrysa Lithari, Manousos A. Klados, Charalambos Bratsas, Christos L. Papadelis, Costas Pappas
2009 Lecture Notes in Computer Science  
Subjects are exposed to pictures selected from the International Affective Picture System (IAPS).  ...  A feature extraction procedure is used to discriminate between four affective states by means of a Mahalanobis distance classifier. The average classifications rate (74.11%) was encouraging.  ...  Then, feature selection took place according to the p-values obtained from the selected features.  ... 
doi:10.1007/978-3-642-02580-8_62 fatcat:k5wbe65dozgefd5pntno6foy2a


Husam Ali
For robots to act more like humans, understand them, and follow their orders in more intelligent ways, they need to understand emotions to make appropriate decisions.  ...  Humans are complicated; understanding only what they say is insufficient for all situations. One complication is that humans express identical emotions in multiple ways.  ...  One approach is to classify features as local or global features. Global features are extracted from the complete signal, while local features are extracted from signals segments.  ... 
doi:10.26782/jmcms.2020.09.00016 fatcat:ejnl2rlatnhhzeotno2ymmjjke

SFE-Net: EEG-based Emotion Recognition with Symmetrical Spatial Feature Extraction [article]

Xiangwen Deng, Junlin Zhu, Shangming Yang
2021 arXiv   pre-print
However, the conventional methods ignore the adjacent and symmetrical characteristics of EEG signals, which also contain salient information related to emotion.  ...  In this paper, a spatial folding ensemble network (SFE-Net) is presented for EEG feature extraction and emotion recognition.  ...  EEG-based emotion recognition methods can be roughly partitioned into two parts: EEG feature extraction and emotion classification.  ... 
arXiv:2104.06308v5 fatcat:266jsy6efzaqtiquof5dqppi6q
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