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1,836 results for “emotion”
Dictionary of Emotional and Nonemotional Frames
<p>This resource is a dictionary of frames-to-emotion associations. It contains multiple FrameNet frames and the strength of their association to "emotionality",i.e., the degree to which a frame expresses an emotion, irrespective of what specific emotion that is.</p>
Masked Emotion FilmClip Dataset (MEFD): Emotion Elicitation with Facial Coverings
<p><strong>Masked Emotion FilmClip Dataset (MEFD): Emotion Elicitation with Facial Coverings</strong></p><p> </p><p>The Masked Emotion FilmClip Dataset (MEFD) stands as an avant-garde assembly of emotion-inducing video clips tailored for a unique niche - the elicitation of emotions in individuals wearing facial masks. This dataset emerges in response to the global need to understand emotional cues and expressions in the backdrop of widespread facial mask usage. Assembled by leveraging the synergies between cinematography and psychological research, MEFD serves as an invaluable trove for researchers, especially those in AI, seeking to decode emotions even when a significant portion of the face is concealed.</p><p><strong>Dataset Highlights</strong>:</p><ul><li><strong>Facial Masks</strong>: All subjects in the video clips are seen wearing facial masks, replicating real-world scenarios and augmenting the dataset's relevance.</li><li><strong>Film Titles</strong>: The title of each selected film enriching the context of the emotional narrative.</li><li><strong>Emotion Label</strong>: Clear emotion classification associated with every clip, ensuring replicability in emotional elicitation.</li><li><strong>Clip Duration</strong>: Precise duration details ensuring standardized exposure and consistent emotion elicitation.</li><li><strong>Curated with Expertise</strong>: Clips have undergone rigorous evaluation by seasoned psychologists and film critics, affirming their effectiveness in eliciting the designated emotion.</li><li><strong>Consent and Ethics</strong>: The dataset respects and upholds privacy and ethical standards. Every participant provided informed consent. This endeavor has received the green light from the Ethics Committee at the University of Granada, documented under the reference: 2100/CEIH/2021.</li></ul><p><strong>Emotion-Eliciting Video Clips within Dataset</strong>:</p><p>Film Targeted Emotion Duration (seconds) The Lover Baseline 43 American History X Anger 106 Cry Freedom Sadness 166 Alive Happiness 310 Scream Fear 395</p><p>A paramount feature of MEFD is its emphasis on "key moments". These timestamps, a product of collective expertise from psychologists and film critics, guide the researcher to intervals of heightened emotional resonance within the clips, especially challenging to discern with masked faces.</p><p><strong>Key Emotional Moments within Dataset</strong>:</p><p>Film Targeted Emotion Key moment timestamps (seconds) American History X Anger 36, 57, 68 Cry Freedom Sadness 112, 132, 154 Alive Happiness 227, 270, 289 Scream Fear 23, 42, 79, 226, 279, 299, 334</p><p> </p><p>-----------------</p><p>DATA STRUCTURE<br>-----------------</p><p>SADNESS_XXX.CSV<br>timestamp emotion<br>1625062890.938222 NEUTRAL --> Initial time start for the neutral video<br>1625062932.567609 SADNESS --> Initial time start for the EMOTION video</p><p><br>Notes:<br>** Subject id 15: FEAR label started to fast; Neutral data very few<br>-----------------</p><p> </p><p><i>The ethical consent for this dataset was provided by La Comisión de Ética en Investigación de la Universidad de Granada, as documented in the approval titled: 'DETECCIÓN AUTOMÁTICA DE LAS EMOCIONES BÁSICAS Y SU INFLUENCIA EN LA TOMA DE DECISIONES MEDIANTE WEARABLES Y MACHINE LEARNING' registered under 2100/CEIH/2021.</i></p><p>MEFD is more than just a dataset; it is a testament to human resilience and adaptability. As facial masks become ubiquitous, understanding the nuances of masked emotional expressions becomes imperative. MEFD rises to this challenge, bridging gaps and pioneering a new frontier in emotion research.</p>
Gilman-Adhered FilmClip Emotion Dataset (GAFED): Tailored Clips for Emotional Elicitation
<p><strong>Gilman-Adhered FilmClip Emotion Dataset (GAFED): Tailored Clips for Emotional Elicitation</strong></p> <p><strong>Description</strong>:</p> <p>Introducing the Gilman-Adhered FilmClip Emotion Dataset (GAFED) - a cutting-edge compilation of video clips curated explicitly based on the guidelines set by Gilman et al. (2017). This dataset is meticulously structured, leveraging both the realms of film and psychological research. The objective is clear: to induce specific emotional responses with utmost precision and reproducibility. Perfectly tuned for researchers, therapists, and educators, GAFED facilitates an in-depth exploration into the human emotional spectrum using the medium of film.</p> <p><strong>Dataset Highlights</strong>:</p> <ul> <li><strong>Gilman's Guidelines</strong>: GAFED's foundation is built upon the rigorous criteria and insights provided by Gilman et al., ensuring methodological accuracy and relevance in emotional elicitation.</li> <li><strong>Film Titles</strong>: Each selected film's title provides an immersive backdrop to the emotions sought to be evoked.</li> <li><strong>Emotion Label</strong>: A focused emotional response is designated for each clip, reinforcing the consistency in elicitation.</li> <li><strong>Clip Duration</strong>: Standardized duration of every clip ensures a uniform exposure, leading to consistent response measurements.</li> <li><strong>Curated with Precision</strong>: Every film clip in GAFED has been reviewed and handpicked, echoing Gilman et al.'s principles, thereby cementing their efficacy in triggering the intended emotion.</li> </ul> <p><strong>Emotion-Eliciting Video Clips within Dataset</strong>:</p> Film Targeted Emotion Duration (seconds) The Lover Baseline 43 American History X Anger 106 Cry Freedom Sadness 166 Alive Happiness 310 Scream Fear 395 <p>The crowning feature of GAFED is its identification of "key moments". These crucial timestamps serve as a bridge between cinema and emotion, guiding researchers to intervals teeming with emotional potency.</p> <p><strong>Key Emotional Moments within Dataset</strong>:</p> Film Targeted Emotion Key moment timestamps (seconds) American History X Anger 36, 57, 68 Cry Freedom Sadness 112, 132, 154 Alive Happiness 227, 270, 289 Scream Fear 23, 42, 79, 226, 279, 299, 334 <p><strong>Based on</strong>: Gilman, T. L., et al. (2017). A film set for the elicitation of emotion in research. Behavior Research Methods, 49(6).</p> <p>GAFED isn't merely a dataset; it's an amalgamation of cinema and psychology, encapsulating the vastness of human emotion. Tailored to perfection and adhering to Gilman et al.'s insights, it stands as a beacon for researchers exploring the depths of human emotion through film.</p>
EmoKey Moments Muse EEG Dataset (EKM-ED): A Comprehensive Collection of Muse S EEG Data and Key Emotional Moments
<p><strong>EmoKey Moments Muse EEG Dataset (EKM-ED): A Comprehensive Collection of Muse S EEG Data and Key Emotional Moments</strong></p> <p> </p> <p><strong>Dataset Description:</strong></p> <p>The EmoKey Moments EEG Dataset (EKM-ED) is an intricately curated dataset amassed from 47 participants, detailing EEG responses as they engage with emotion-eliciting video clips. Covering a spectrum of emotions, this dataset holds immense value for those diving deep into human cognitive responses, psychological research, and emotion-based analyses.</p> <p><strong>Dataset Highlights:</strong></p> <ol> <li><strong>Precise Timestamps</strong>: Capturing the exact millisecond of EEG data acquisition, ensuring unparalleled granularity.</li> <li><strong>Brainwave Metrics</strong>: Illuminating the variety of cognitive states through the prism of Delta, Theta, Alpha, Beta, and Gamma waves.</li> <li><strong>Motion Data</strong>: Encompassing the device's movement in three dimensions for enhanced contextuality.</li> <li><strong>Auxiliary Indicators</strong>: Key elements like the device's positioning, battery metrics, and user-specific actions are meticulously logged.</li> <li><strong>Consent and Ethics</strong>: The dataset respects and upholds privacy and ethical standards. Every participant provided informed consent. This endeavor has received the green light from the Ethics Committee at the University of Granada, documented under the reference: 2100/CEIH/2021.</li> </ol> <p>A pivotal component of this dataset is its focus on "key moments" within the selected video clips, honing in on periods anticipated to evoke heightened emotional responses.</p> <p><strong>Curated Video Clips within Dataset:</strong></p> Film Emotion Duration (seconds) The Lover Baseline 43 American History X Anger 106 Cry Freedom Sadness 166 Alive Happiness 310 Scream Fear 395 <p>The cornerstone of EKM-ED is its innovative emphasis on these key moments, bringing to light the correlation between distinct cinematic events and specific EEG responses.</p> <p><strong>Key Emotional Moments in Dataset:</strong></p> Film Emotion Key moment timestamps (seconds) American History X Anger 36, 57, 68 Cry Freedom Sadness 112, 132, 154 Alive Happiness 227, 270, 289 Scream Fear 23, 42, 79, 226, 279, 299, 334 <p>Citation:<br> Gilman, T. L., et al. (2017). A film set for the elicitation of emotion in research. Behavior Research Methods, 49(6).<br> <a href="https://doi.org/10.3758/s13428-016-0842-x">Link to the study</a></p> <p>With its unparalleled depth and focus, the EmoKey Moments EEG Dataset aims to advance research in fields such as neuroscience, psychology, and affective computing, providing a comprehensive platform for understanding and analyzing human emotions through EEG data.<br> <br> <br> </p> <p>———————————————————————————————————<br> FOLDER STRUCTURE DESCRIPTION<br> ———————————————————————————————————</p> <p>- questionnaires: all there response questionnaires (Spanish); raw and preprocessed<br> Including SAM<br> |<br> ——preprocessed: Ficha_Evaluacion_Participante_SAM_Refactored.csv: the SAM responses for every film clip</p> <p><br> - key_moments: the key moment timestamps for every emotion’s clip</p> <p>- muse_wearable_data: XXXX<br> |<br> |—raw<br> |——1: ID = 1 of subject<br> |————muse: EEG data of Muse device<br> |—————————ANGER_XXX.csv : leg data of the anger elicitation<br> |—————————FEAR_XXX.csv : leg data of the fear elicitation<br> |—————————HAPPINESS_XXX.csv : leg data of the happiness elicitation<br> |—————————SADNESS_XXX.csv : leg data of the sadness elicitation<br> |————order: film elicitation order of play: For example: HAPPINESS,SADNESS,ANGER,FEAR<br> …<br> |<br> |—preprocessed<br> |——unclean-signals: without removing EEG artifacts, noise, etc.<br> |————muse: EEG data of Muse device<br> |—————————0.0078125: data downsampled to 128 Hz from 256Hz recorded<br> |——clean-signals: removed EEG artifacts, noise, etc.<br> |————muse: EEG data of Muse device<br> |—————————0.0078125: data downsampled to 128 Hz from 256Hz recorded<br> <br> <br> <em>The ethical consent for this dataset was provided by La Comisión de Ética en Investigación de la Universidad de Granada, as documented in the approval titled: 'DETECCIÓN AUTOMÁTICA DE LAS EMOCIONES BÁSICAS Y SU INFLUENCIA EN LA TOMA DE DECISIONES MEDIANTE WEARABLES Y MACHINE LEARNING' registered under 2100/CEIH/2021.</em></p>
Tantrum Tamers 2.0: The Role of Emotion
ClinicalTrials.gov study NCT03597789. IPD Sharing: YES. Countries: 1. Publications: 5.
Neural Mechanisms of Enhancing Emotion Regulation in Bereaved Spouses
ClinicalTrials.gov study NCT04822194. IPD Sharing: YES. Countries: 1. Publications: 7.
The Effect of Surgeon Emotional Support on Treatment Choice for Low-risk Thyroid Cancer
ClinicalTrials.gov study NCT05132478. IPD Sharing: YES. Countries: 1. Publications: 0.
Social bonding through shared experiences: The role of emotional intensity
Open the record for dataset details and reuse information.
Data for Positive Emotion Regulation Task
<p>These files contain the .mul EEG and .evt output files from BESA used in the positive emotion regulation task.</p>
Emotion recognition HD - Data and R code
<p>Data and R code - <strong>Recognition of emotion from subtle and non-stereotypical dynamic facial expressions in Huntington's diseaseRecognition of emotion from subtle and non-stereotypical dynamic facial expressions in Huntington's disease</strong></p>
Knocking Sound Effects With Emotional Intentions
<p>The dataset was recorded by the professional foley artist Ulf Olausson at the FoleyWorks (http://foleyworks.se/) studios in Stockholm on the 15th October, 2019. Inspired by previous work on knocking sounds [1]. we chose five type of emotions to be portrayed in the dataset: anger, fear, happiness, neutral and sadness.</p> <p>In order to imagine a situation where the knocking action is performed with a particular emotional intention, we provided the foley artist with common situations where these actions can happen. The emotions, alongside the context are the following:</p> <ul> <li><strong>Anger</strong>: telling a flatmate for the 4th time to turn down the very loud music.</li> <li><strong>Fear</strong>: alerting a neighbor of a possible risk.</li> <li><strong>Happiness</strong>: telling a flatmate you won a prize.</li> <li><strong>Neutral</strong>: parcel delivery.</li> <li><strong>Sadness</strong>: telling a friend someone passed away.</li> </ul> <p>We also encouraged the foley artist to perform diverse interpretations of the context provided in order to have a wider variety of sounds. The dataset was recorded with a Rode NT1 microphone, performing the knocks to a closed door.</p> <p>We recorded a total of 600 knocking actions (120 actions per category). An action is a sequence of individual knocks. We discarded 20 actions per category to filter out unwanted noise. The final 500 audio files were edited only to trim the audio so each file starts on the first knock onset and finish on the last knock decay. </p> <p> </p> <p> </p> <p>[1] R. Vitale and R. Bresin, “Emotional Cues in Knocking Sounds,” in 10th International Conference on Music Perception and Cognition, Sapporo, Japan, August 25-29, 2008, 2008, p. 276.</p>
EmoSynth: The Emotional Synthetic Audio Dataset
<p>The ability of sound to enhance human wellbeing has been known since ancient civilisations, and methods can be found today across domains of health and within a variety of cultures. </p> <p>EmoSynth is a dataset of 144 audio files which have been labelled by 40 listeners for their the perceived emotion, in regards to the dimensions of Valence and Arousal.</p> <p>Results on the dataset show that Arousal does correlate moderately to fundamental frequency, and that the sine waveform is perceived as significantly different to square and sawtooth waveforms when evaluating perceived Arousal. The general results suggest that isolated synthetic audio can be modelled as a means of evoking affective states of emotion.</p> <p>When using this version of the data for a publication, please cite the following study: </p> <pre>@inproceedings{Baird:2018:PEI:3243274.3243277, author = {Baird, Alice and Parada-Cabaleiro, Emilia and Fraser, Cameron and Hantke, Simone and Schuller, Bj\"{o}rn}, title = {The Perceived Emotion of Isolated Synthetic Audio: The EmoSynth Dataset and Results}, booktitle = {Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion}, series = {AM'18}, year = {2018}, isbn = {978-1-4503-6609-0}, location = {Wrexham, United Kingdom}, pages = {7:1--7:8}, articleno = {7}, numpages = {8}, url = {http://doi.acm.org/10.1145/3243274.3243277}, doi = {10.1145/3243274.3243277}, acmid = {3243277}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {affective computing, perception, sound healing, synthetic audio, wellbeing}, } </pre> <p> </p>
Datasets for article: The emotional and attentional impact of exposure to one’s own body in bulimia nervosa: A physiological view
<p>The data of the article titled: <strong>The emotional and attentional impact of exposure to one’s own body in bulimia nervosa: A physiological view</strong></p> <p>submitted to PLOS ONE </p>
OLAF, Open Library of Affective Foods: A tool to investigate the emotional impact of food in adolescents
<p>The Open Library of Affective Foods (OLAF) is a set of original food pictures created to reliably select food pictures based on the emotions they prompt, as indicated by affective ratings of valence, arousal, and dominance and by an additional food craving scale. OLAF images were designed to allow simultaneous use with affective images from the International Affective Picture System (IAPS), which is a well-known instrument to investigate emotional reactions in the laboratory. The ultimate goal of the OLAF is to contribute to understanding how food is emotionally processed in healthy individuals and in patients who suffer from eating and weight-related disorders.</p> <p>The present normative data, which was based on a large sample of an adolescent population, indicate that when viewing affective non-food IAPS images, valence, arousal, and dominance ratings were in line with expected patterns based on previous emotion research. Moreover, when viewing food pictures, affective and food craving ratings were consistent with research on food cue processing. As a whole, the data supported the methodological and theoretical reliability of the OLAF ratings, therefore providing researchers with a standardized tool to reliably investigate the emotional and motivational significance of food.</p>
dataset for article: Ortega-Roldan, B., Rodriguez-Ruiz, S., Perakakis, P., Fernandez-Santaella, M.C., Vila, J. (2014). The emotional and attentional impact of exposure to one’s own body in bulimia nervosa: A physiological view. PLoS ONE 9(7): e102595
<p>dataset for article: Ortega-Roldan, B., Rodriguez-Ruiz, S., Perakakis, P., Fernandez-Santaella, M.C., Vila, J. (2014). The emotional and attentional impact of exposure to one’s own body in bulimia nervosa: A physiological view. PLoS ONE 9(7): e102595</p>
Evidence of Emotion-Antecedent Appraisal Checks in Electroencephalography and Facial Electromyography
<p>Data from study published in Plos One</p>
Age-dependent positivity bias in children's perception of emotion terms
<p>Data set of Bahn, D., Vesker, M., Garcia Alanis, J. C., Schwarzer, G., & Kauschke, C. (in press). Age-dependent positivity bias in children’s perception of emotion terms. <em>Frontiers in Psychology</em>. doi: 10.3389/fpsyg.2017.01268</p>
Pretrained features for Ekman6 and VideoEmotion8 video emotion datasets
Open the record for dataset details and reuse information.
Emotional body motion data
<p>- The current dataset contains emotional body motion data according to seven emotional statuses of happy, sad, surprised, angry, disgust, fearful, and neutral that was performed by participants with no prior experience in acting who shared same cultural background of the East Asian (Korean). Body motion data consists of 3 coordinates (x, y, and z) of global position data of 19 body parts (hip, spine, spine_1, neck, head, shoulders, arms, forearms, hands, legs, knees, and feet) in 150 frames (30 frames per second * 5 seconds) according to 7 emotions (happy, sad, surprise, angry, disgust, fearful, neutral). There are total of 29 participant's 20 trials according to 7 emotion category (29*20*7=4060). Each data is labeled by "{participant Number}_{Block}_{Trial}_{Emotion; 1: happy 2: sad 3: surprised 4: angry 5: disgust 6: fearful 7: neutral}.csv"</p><p>- An additional 13 participants were recruited (who did not participated in gathering body motion) to assess the emotional status of each body motion using biological motion. This result of human evaluation (each participant's answer toward each body motion) is described in the "Human Evaluation result.xlsx" file</p>
Use of artificial intelligence techniques for the recognition of human emotions: a bibliometric analysis
<p>Human emotion recognition with AI uses physiological, audiovisual, and linguistic signals. Despite its importance and great progress in emotion recognition, several challenges remain in generalization and evaluation through standards and shared data, as well as other research gaps. Therefore, the objective is to analyze the scientific production on the use of artificial intelligence techniques for the recognition of human emotions. This study uses bibliometric analysis following the guidelines of the PRISMA-2020 statement for literature reviews. Based on the results of the bibliometrics on the use of artificial intelligence techniques in for the recognition of human emotions, significant conclusions are obtained that improve the understanding of the current panorama in this field of research. A growing interest in the subject is observed during the years 2023, 2022, 2021 and 2020, which demonstrates the relevance and potential of artificial intelligence in the recognition of human emotions. A cubic polynomial growth in the number of scientific articles is observed, demonstrating a constant expansion of knowledge and support for future trends. Leading authors and journals are identified, highlighting global collaboration in China and India. The thematic evolution shows maturity and progressive specialization, with emerging concepts that promise future research and innovative applications.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.