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13 results for “Emotion Classification”
Figure 9. Classification accuracy regardless the ethnic group (Total accuracy 75%)-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Our experiments show that, the impact of ethnic group on the accuracy of emotions<br> recognition is a positive where the accuracy of emotion recognition considering ethnic group is<br> 83.3% as shown in Figure 8, and we got 75% of accuracy regardless ethnic group as shown in<br> Figure 9.</p>
Figure 8. Classification accuracy of emotions considering the ethnic group-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>To study the accuracy of emotion recognition for our approach regardless the ethnic group<br> we used 108 images for the training representing six emotions of six persons. For testing, we used<br> 36 images representing six emotions of six persons.<br> On the other hand, to study the accuracy of emotion recognition for our approach<br> considering the ethnic group we used 36 images for training for each ethnic group representing six<br> emotions of six persons, and test the classifier by using 12 images representing six emotions of six<br> persons.</p>
Figure 3. Electrode placement diagram-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>Reference electrode placed AFz placed in between AF1 and AF2 electrode and ground<br> electrode Oz is placed between O1 and O2 electrodes. The impedance of the electrode range is<br> 5KΩ. The sampling rate was fixed range between 256 samples per second for all the channels.<br> Figure 3 shown in below which is represent the electrode placement diagram. The recorded EEG<br> signal is used to recognize the different level of emotions.</p>
Figure 2. Emotion recognition using EEG-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>This section describes that collection of EEG signals for different emotion recognition<br> experiments. The electroencephalography signals of 32 participants were recorded during one<br> minute videos. Based on that participants are rated in terms of valence and arousal, like/dislike,<br> familiarities and dominance. Emotion related ratings are given based on the online self assessment<br> which is 120 one minute extracted music videos, which are rated by 14-16 volunteers based on<br> arousal and valence. The EEG signals were recorded using 64 electrodes, first 62 electrodes are<br> active electrode, one for reference and remaining one is ground electrode. All the electrodes are<br> placed on the scalp which is made up of the Ag/Ag-Cl. Figure 2 shown in below which is represent<br> the basic EEG signal recording methods and emotion analysis process.</p>
Figure 1. Brain Structure-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>EEG data have collected from<br> desirable subjects. Each and every EEG signal has different kind of bands like Alpha, Beta,<br> Gamma, Theta, and Delta. Each band stores the particular information about the emotions. Alpha<br> band (8-13 Hz) which located in Frontal Occipital, Beta band (13-30 Hz) which located in Frontal<br> Central, Gamma band (30-100 Hz), Theta band (4- 7 Hz) which located in Midline Temp, Delta<br> band (0-4Hz) which located in Frontal Lobe. Before processing the EEG signal and extracting these<br> bands, preprocess the signal and reduce the noise. The basic brain figure is shown in below.</p>
Figure 8. Sample Emotion Classification Result-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>For all the 32 participants the EEG signal has to be sampled and process their emotions. The<br> emotions are depends on the music and video clips. In this paper the video clips are changed from<br> one person to other person. Here hybrid feed forward neural networks with radial basis function;<br> probabilistic neural network classifier is used to classify the emotions from EEG. PNN is very fast<br> and insensitive neural network which provides the optimized classification result. Compare to the<br> multi layer perception neural network it provide accurate result. It classifies the emotion into two<br> different groups like arousal and valence. Figure 8 shows that the model implemented result of<br> emotion classification and person identification.</p>
Figure 7. Neural Network model-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>In Probabilistic Neural Network the operations are organized into a multilayer feed forward<br> neural network with four layers like input layer, hidden layer, pattern layer and output layer. PNN<br> use the Euclidean distance measure the difference between one neuron to other neurons. The actual<br> target values are stored in the hidden neuron and the optimized weighted values are fed into the<br> same category hidden neuron. Then finally the output layer compared the weighted votes of each<br> target values and the target votes are used to predict the emotions.</p>
Figure 10. Sensitivity and specificity of different bands-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>Figure10 has shown in sensitivity and specificity of different neural network which is used<br> to explain how exactly the emotions are classified into groups and accuracy value shown in above<br> table 3.</p>
Figure 9. Mean Square Error for different bands-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>Figure 9 has shown in different epochs using neural networks with mean square error<br> performance and the Table 3 shows that different bands mean square error values while training the<br> neural networks with particle swarm optimization.</p>
Figure 6. Mean square error of alpha band-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>Particle swarm optimization algorithm first optimizes the neural networks weight and bias<br> and provides the minimum mean square error with nearer by zero. The following figure 6 has<br> shown that minimum mean square error when training the particular band features.</p>
Figure 5. Process flow of PSO-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>PSO is used to identify the best solution from collection of solution. It is a computational<br> method that optimizes a problem by iteratively trying to improve a candidate solution with regard to<br> a given measure of quality. PSO optimizes a problem by having a population of candidate solutions,<br> here dubbed particles, and moving these particles around in the search-space according to simple<br> mathematical formulae over the particle's position and velocity. Each particle's movement is<br> influenced by its local best known position but, is also guided toward the best known positions in<br> the search-space, which are updated as better positions are found by other particles. This is expected<br> to move the swarm toward the best solutions. PSO is a metaheuristic as it makes few or no<br> assumptions about the problem being optimized and can search very large spaces of candidate<br> solutions. However, metaheuristic such as PSO do not guarantee an optimal solution is ever found.<br> The following Figure 5 explains the basic flow of PSO process.</p>
VERBS EXPRESSING EMOTIONAL RELATIONSHIP AND THEIR SEMANTIC CLASSIFICATION.
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EEGEMO: a Wearable EEG Dataset for Online Emotion Classification
<p>Electroencephalography (EEG) -based emotion recognition is being widely applied because it measures electrical correlates directly from the brain rather than the indirect measurement of other physiological responses initiated by the brain. The recent development of non-invasive and portable EEG sensors makes it possible to use them in real-time applications. This dataset contains EEG data of 15 participants from two commercial EEG devices: Muse S headband and Neurosity Crown. The participants watched a total of 16 short videos which elicited emotions from the valence arousal domain. We developed a lightweight emotion classification pipeline from this dataset which could be used in real-time in the future. Moreover, the dataset is compatible with the state-of-art AMIGOS dataset, and the developed pipeline outperforms the classification results obtained from the AMIGOS dataset.</p>
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