Emotion recognition based on wavelet analysis of Empirical Mode Decomposed EEG signals responsive to music videos

Shahnaz, Celia; Shoaib-Bin-Masud,; Hasan, S. M. Shafiul

doi:10.1109/tencon.2016.7848034

“…The recognition of mental fatigue was found to be efficient when adaptive weights switch in deep belief networks (DBN) [22,23]. Even though DBN has also been applied in the recognition of emotion more studies use SVM which combines feature smoothing or selection methods, such as canonical correlation analysis (CCA) and principal component analysis (PCA) [24][25][26]. An end-to-end model based on CNN is used to reduce the cost of designing the feature set, and as a result, the average accuracy of 0.7548 was reported [27].…”

Section: Related Workmentioning

confidence: 99%

“…By using a fast Fourier transformation, the frequency features (60 power features, 16 power difference features) were prepared. In each channel, the power features were computed on four frequency bands, i.e., theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). Power difference features were employed to detect the variation in cerebral activity between the left and right cortical areas.…”

Section: Feature Extraction and The Target Emotion Classesmentioning

confidence: 99%

Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

Cai

¹

,

Chen

²

,

Yin

³

2019

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Feature selection plays a crucial role in analyzing huge-volume, high-dimensional EEG signals in human-centered automation systems. However, classical feature selection methods pay little attention to transferring cross-subject information for emotions. To perform cross-subject emotion recognition, a classifier able to utilize EEG data to train a general model suitable for different subjects is needed. However, existing methods are imprecise due to the fact that the effective feelings of individuals are personalized. In this work, the cross-subject emotion recognition model on both binary and multi affective states are developed based on the newly designed multiple transferable recursive feature elimination (M-TRFE). M-TRFE manages not only a stricter feature selection of all subjects to discover the most robust features but also a unique subject selection to decide the most trusted subjects for certain emotions. Via a least square support vector machine (LSSVM), the overall multi (joy, peace, anger and depression) classification accuracy of the proposed M-TRFE reaches 0.6513, outperforming all other methods used or referenced in this paper.

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“…Where the input weight ( ) multiplied by the previous hidden layer's weight range (ℎ )with the input ( ) added with bias. Meanwhile, the function of the cell decides the value of the vector that renew the value using (7).…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Emotion and Attention of Neuromarketing Using Wavelet and Recurrent Neural Networks

Rasyid¹,

Djamal²

2019

EECSI

0

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One method concerning evaluating video ads is neuromarketing. This information comes from the viewer's mind, thus minimizing subjectivity. Besides, neuromarketing can overcome the difficulties of respondents who sometimes do not know the response to the video ads they watch. Neuromarketing involves information from what customers think is captured by an Electroencephalogram (EEG) device. Meanwhile, if the electrical signal information in the brain is corresponding to an understanding of one's psychology, it is called neuropsychology. This field consists of emotions, attention, and concentration variables. This research offered neuromarketing base on emotion and attention of EEG signal using Wavelet extraction and Recurrent Neural Networks. Measurement of EEG variable every two seconds while watching video ads. The results showed that Wavelet and Recurrent Neural Networks could provide training data accuracy of 100% and 89.73% for new data. The experiment also gave that the RMSprop optimization model for the weight correction contributed to higher correctness of 1.34% than the Adam model. Meanwhile, using Wavelet for extraction can increase accuracy by 5%.

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“…EEG signals have been used in the classification of emotions using Wavelet and SVM as stimulation of music videos [7]. Other studies sought the relationship between EEG signals and feelings with a linear dynamic system approach [8].…”

Section: Introductionmentioning

confidence: 99%

Emotion and Attention of Neuromarketing Using Wavelet and Recurrent Neural Networks

Rasyid¹,

Djamal²

2019

EECSI

View full text Add to dashboard Cite

One method concerning evaluating video ads is neuromarketing. This information comes from the viewer's mind, thus minimizing subjectivity. Besides, neuromarketing can overcome the difficulties of respondents who sometimes do not know the response to the video ads they watch. Neuromarketing involves information from what customers think is captured by an Electroencephalogram (EEG) device. Meanwhile, if the electrical signal information in the brain is corresponding to an understanding of one's psychology, it is called neuropsychology. This field consists of emotions, attention, and concentration variables. This research offered neuromarketing base on emotion and attention of EEG signal using Wavelet extraction and Recurrent Neural Networks. Measurement of EEG variable every two seconds while watching video ads. The results showed that Wavelet and Recurrent Neural Networks could provide training data accuracy of 100% and 89.73% for new data. The experiment also gave that the RMSprop optimization model for the weight correction contributed to higher correctness of 1.34% than the Adam model. Meanwhile, using Wavelet for extraction can increase accuracy by 5%.

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Emotion recognition based on wavelet analysis of Empirical Mode Decomposed EEG signals responsive to music videos

Cited by 34 publications

References 12 publications

Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

Emotion and Attention of Neuromarketing Using Wavelet and Recurrent Neural Networks

Emotion and Attention of Neuromarketing Using Wavelet and Recurrent Neural Networks

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