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16 results for “emotional signals”
Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" (Data set and materials used for human-robot interaction experiment)
<p>Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" authored by Troels Aske Klausen, Ulrich Farhadi, Evgenios Vlachos, and Jonas Jørgensen.</p> <p>Contents of set 2:<br> - Data set and materials used for the human-robot interaction experiment and for data analysis</p> <p>Files:<br> - "Questionnaire.pdf": Questionnaire used for data collection.<br> - "Video links.txt": Weblinks to stimuli videos used.<br> - "Data set.xls": Collected raw data.<br> - "Matlab_DataAnalysis.mlx": Matlab script used to analyze raw data.<br> - "Linear_Arousal.png": Linear fit between the scoring of arousal and BPM.<br> - "Linear_Dominance.png": Linear fit between the scoring of dominance and BPM.<br> - "Linear_Pleasure.png": Linear fit between the scoring of pleasure and BPM.</p> <p>The experiment procedure is described in the paper.<br> The soft robot used for the experiment is open source and can be manufactured using design files available on Zenodo: 10.5281/zenodo.5565201</p>
EmoPairCompete - Physiological Signals Dataset for Emotion and Frustration Assessment under Team and Competitive Behaviours
<p>Please refer to the documentation at: https://github.com/DTUComputeStatisticsAndDataAnalysis/EmoPairCompete</p>
Figure 4. Extracted bands diagramClassification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>Based on that coefficient, different bands have to be extracted. The bands are alpha, beta,<br> theta, gamma, and delta. Figure 4 shown in below which is represent the different extract band<br> diagrams.</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>
GamER: Gameplay Physiological Signal Dataset for Emotion Recognition
<p>A dataset of physiological signals (EEG, ECG, EDA, EOG and Respiration) collected from 48 subjects while they were playing simple games designed to elicit emotional responses.</p> <p>Data were collected using the Biosignals PLUX Researcher kit, which allows up to 10 hours of signal recordings at up to 3kHz sampling rate and 16-bit resolution per channel, while recording data from up to eight sensors simultaneously.</p> <p>The dataset consists of 3 files:</p> <ol> <li>data.zip: Contains the physiological signals. They are organized in 1 folder per subject, which contains the recorded data in .h5 and .txt format as provided by the Researcher kit.</li> <li>emotion_annotation.csv: Contains the emotion annotation that was produced by a psychologist based on the subjects self-reports and by examining the video of the data collection process (the videos are not included for privacy reasons)</li> <li>video_offsets.csv: Additional information for synchronizing the video-based annotations with the signals.</li> </ol> <p>For more information, refer to the accompanying publication.</p> <p><em>Disclaimer: All subjects have provided written consent for the publication of their anonymized physiological signals.</em></p>
Visual and olfactory signals of conspecifics induce emotional contagion in mice
Open the record for dataset details and reuse information.
Acquisition and Analysis of Relationships Between Longitudinal Emotional Signals Produced by an Artificial Intelligence Algorithm and Self-questionnaires Used in the Psychiatric Follow-up of Patients
ClinicalTrials.gov study NCT05988840. IPD Sharing: NO. Countries: 0. Publications: 0.
Meningeal immune signaling supports emotional adaptation following threat
GEO Series GSE276662. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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