M
Mohammad Soleymani
Researcher at University of Southern California
Publications - 122
Citations - 8802
Mohammad Soleymani is an academic researcher from University of Southern California. The author has contributed to research in topics: Computer science & Affective computing. The author has an hindex of 28, co-authored 106 publications receiving 6378 citations. Previous affiliations of Mohammad Soleymani include University of Rochester & University of Geneva.
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Journal ArticleDOI
DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Sander Koelstra,Christian Mühl,Mohammad Soleymani,Jong-Seok Lee,Ashkan Yazdani,Touradj Ebrahimi,Thierry Pun,Anton Nijholt,Ioannis Patras +8 more
TL;DR: A multimodal data set for the analysis of human affective states was presented and a novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool.
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A Multimodal Database for Affect Recognition and Implicit Tagging
TL;DR: Results show the potential uses of the recorded modalities and the significance of the emotion elicitation protocol and single modality and modality fusion results for both emotion recognition and implicit tagging experiments are reported.
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Multimodal Emotion Recognition in Response to Videos
TL;DR: The results over a population of 24 participants demonstrate that user-independent emotion recognition can outperform individual self-reports for arousal assessments and do not underperform for valence assessments.
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Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection
TL;DR: The effect of the contamination of facial muscle activities on EEG signals is analyzed and it is found that most of the emotionally valuable content in EEG features are as a result of this contamination, however, the statistical analysis showed that EEG signals still carry complementary information in presence of facial expressions.
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A survey of multimodal sentiment analysis
Mohammad Soleymani,David Garcia,Brendan Jou,Björn Schuller,Björn Schuller,Björn Schuller,Shih-Fu Chang,Maja Pantic,Maja Pantic +8 more
TL;DR: The thesis is that multimodal sentiment analysis holds a significant untapped potential with the arrival of complementary data streams for improving and going beyond text-based sentiment analysis.