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Ashkan Yazdani
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 30
Citations - 3820
Ashkan Yazdani is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Feature vector & Feature extraction. The author has an hindex of 15, co-authored 30 publications receiving 2754 citations. Previous affiliations of Ashkan Yazdani include University of Tehran & École Normale Supérieure.
Papers
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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.
Book ChapterDOI
Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos
Sander Koelstra,Ashkan Yazdani,Mohammad Soleymani,Christian Mühl,Jong-Seok Lee,Anton Nijholt,Thierry Pun,Touradj Ebrahimi,Ioannis Patras +8 more
TL;DR: This work presents some promising results of its research in classification of emotions induced by watching music videos, and shows robust correlations between users' self-assessments of arousal and valence and the frequency powers of their EEG activity.
Proceedings ArticleDOI
Classification of EEG signals using Dempster Shafer theory and a k-nearest neighbor classifier
TL;DR: It is shown that the Dempster-Shafer KNN classifier achieves a higher correct classification rate than the classical voting KNN Classifier and the distance-weighted KNNclassifier.
Book ChapterDOI
EEG correlates of different emotional states elicited during watching music videos
TL;DR: The results of analyzing the Electroencephalogram (EEG) for assessing emotions elicited during watching various pre-selected emotional music video clips have been reported and in-depth results of both subject-dependent and subjectindependent correlation analysis between time domain, and frequency domain features of EEG signal and subjects' self assessed emotions are produced.
Proceedings ArticleDOI
Implicit emotional tagging of multimedia using EEG signals and brain computer interface
TL;DR: It is shown that the proposed brain-computer interface (BCI) system based on P300 evoked potential can successfully perform implicit emotional tagging and naïve subjects who have not participated in training of the system can also use it efficiently.