Emotion Recognition Using EEG Signal Based on Support Vector Machine and Highly Reliable Validation Set

2019 ◽  
Author(s):  
Chang-Yuan He ◽  
Wai-Chi Fang
Keyword(s):  
Support Vector Machine ◽  
Emotion Recognition ◽  
Support Vector ◽  
Eeg Signal ◽  
Validation Set

scholarly journals Emotion Recognition in Speech Using with SVM, DSVM and Auto-Encoder

2021 ◽  
Vol 9 (8) ◽  
pp. 1021-1026
Author(s):  
Jeena Augustine
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Emotion Recognition ◽  
Support Vector ◽  
Mel Frequency Cepstral Coefficients ◽  
Classification Rate ◽  
Signal Process ◽  
Common Technique ◽  
Audio Information ◽  
Deep Support

Abstract: Emotions recognition from the speech is one of the foremost vital subdomains within the sphere of signal process. during this work, our system may be a two-stage approach, particularly feature extraction, and classification engine. Firstly, 2 sets of options square measure investigated that are: thirty-nine Mel-frequency Cepstral coefficients (MFCC) and sixty-five MFCC options extracted supported the work of [20]. Secondly, we've got a bent to use the Support Vector Machine (SVM) because the most classifier engine since it is the foremost common technique within the sector of speech recognition. Besides that, we've a tendency to research the importance of the recent advances in machine learning along with the deep kerne learning, further because the numerous types of auto-encoders (the basic auto-encoder and also the stacked autoencoder). an oversized set of experiments unit conducted on the SAVEE audio information. The experimental results show that the DSVM technique outperforms the standard SVM with a classification rate of sixty-nine. 84% and 68.25% victimization thirty-nine MFCC, severally. To boot, the auto encoder technique outperforms the standard SVM, yielding a classification rate of 73.01%. Keywords: Emotion recognition, MFCC, SVM, Deep Support Vector Machine, Basic auto-encoder, Stacked Auto encode

scholarly journals Seizure Type Classification on EEG Signal using Support Vector Machine

2019 ◽  
Vol 1201 ◽  
pp. 012065 ◽  
Author(s):  
Inggi Ramadhani Dwi Saputro ◽  
Nita Dwi Maryati ◽  
Siti Rizqia Solihati ◽  
Inung Wijayanto ◽  
Sugondo Hadiyoso ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Seizure Type ◽  
Eeg Signal ◽  
Type Classification

scholarly journals Improving Session-to-session Transfer Performance of Emotion Recognition Using Adaptive Support Vector Machine

2020 ◽  
Vol 1601 ◽  
pp. 042028
Author(s):  
Kai Yang ◽  
Guangcheng Bao ◽  
Ying Zeng ◽  
Li Tong ◽  
Jun Shu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Emotion Recognition ◽  
Support Vector ◽  
Transfer Performance ◽  
Adaptive Support

scholarly journals Eye-Tracking Analysis for Emotion Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Paweł Tarnowski ◽  
Marcin Kołodziej ◽  
Andrzej Majkowski ◽  
Remigiusz Jan Rak
Keyword(s):  
Support Vector Machine ◽  
Eye Movements ◽  
Eye Tracking ◽  
Emotion Recognition ◽  
Classification Accuracy ◽  
Pupil Diameter ◽  
Support Vector ◽  
Svm Classifier ◽  
High Arousal ◽  
Validation Method

This article reports the results of the study related to emotion recognition by using eye-tracking. Emotions were evoked by presenting a dynamic movie material in the form of 21 video fragments. Eye-tracking signals recorded from 30 participants were used to calculate 18 features associated with eye movements (fixations and saccades) and pupil diameter. To ensure that the features were related to emotions, we investigated the influence of luminance and the dynamics of the presented movies. Three classes of emotions were considered: high arousal and low valence, low arousal and moderate valence, and high arousal and high valence. A maximum of 80% classification accuracy was obtained using the support vector machine (SVM) classifier and leave-one-subject-out validation method.

Emotion Recognition in Elderly Based on SpO2 and Pulse Rate Signals Using Support Vector Machine

2018 ◽  
Author(s):  
Lutfi Hakim ◽  
Adhi Dharma Wibawa ◽  
Evi Septiana Pane ◽  
Mauridhi Hery Purnomo
Keyword(s):  
Support Vector Machine ◽  
Emotion Recognition ◽  
Pulse Rate ◽  
Support Vector

Speech Emotion Recognition based on Optimized Support Vector Machine

2012 ◽  
Vol 7 (12) ◽  
Author(s):  
Bo Yu ◽  
HaiFeng Li ◽  
ChunYing Fang
Keyword(s):  
Support Vector Machine ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Support Vector
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