WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 56Citation - Scopus: 69Synchrosqueezing Transform Based Feature Extraction From EEG Signals for Emotional State Prediction(Elsevier Sci Ltd, 2019-07) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentThis paper presents a novel method for emotion recognition based on time-frequency analysis using multivariate synchrosqueezing transform (MSST) of multichannel electroencephalography (EEG) signals. With the advancements of the multichannel sensor applications, the need for multivariate algorithms has become obvious for extracting features that stem from multichannel dependency in addition to mono-channel features. In order to model the joint oscillatory structure of these multichannel signals, MSST has recently been proposed. It uses the concepts of joint instantaneous frequency and bandwidth. Electrophysiological data processing mostly requires joint time-frequency analysis in addition to both time and frequency analysis separately. The short-time Fourier transform (STFT) and wavelet transform (WT) are the main approaches utilized in time-frequency analysis. In this paper, the feasibility and performance of multivariate wavelet-based synchrosqueezing algorithm was demonstrated on EEG signals obtained from publically available DEAP database by comparing with its univariate version. Eight emotional states were considered by combining arousal-valence and dominance dimensions. Using linear support vector machines (SVM) as a classifier, MSST and its univariate version resulted in the highest prediction accuracy rates of (9) over tilde3% among all emotional states. (C) 2019 Elsevier Ltd. All rights reserved.Article Citation - WoS: 4Citation - Scopus: 5Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition(Istanbul Univ-Cerrahapasa, 2018-08-03) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentEmotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms. Currently, in addition to classical time-frequency methods, emotional state data have been processed via data-driven methods such as empirical mode decomposition (EMD). Despite its various benefits, EMD has several drawbacks: it is intended for univariate data; it is prone to mode mixing; and the number of local extrema must be enough before the EMD process can begin. To overcome these problems, this study employs a multivariate EMD and its noise-assisted version in the emotional state classification of electroencephalogram signals. Emotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms. Currently, in addition to classical time-frequency methods, emotional state data have been processed via data-driven methods such as empirical mode decomposition (EMD). Despite its various benefits, EMD has several drawbacks: it is intended for univariate data; it is prone to mode mixing; and the number of local extrema must be enough before the EMD process can begin. To overcome these problems, this study employs a multivariate EMD and its noise-assisted version in the emotional state classification of electroencephalogram signals.Conference Object Multivariate Pseudo Wigner Ville Distribution Based Emotion Detection From Electrical Activity of Brain(IEEE, 2017) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentRecently, there has been a rapid development in multivariate signal analysis to determine joint oscillations for multiple data channels. The emotion elicitation in an electroencephalogram (EEG) is a novel area to evaluate methods for emotional differences from brain signals. In this paper, utilizing the idea of joint instantaneous frequency of multivariate data, a multivariate extension of pseudo Wigner distribution is used for emotion recognition from EEG signals, in which different window sizes are employed to interpret the results. As a preliminary study, the best results are obtained as 90%, 75%, 65% in terms of valence, arousal and dominance scale respectively for larger window size.Article Citation - WoS: 14Citation - Scopus: 13Iterative Image Reconstruction Using Non-Local Means With Total Variation From Insufficient Projection Data(Ios Press, 2016-02-01) Ertas, Metin; Yildirim, Isa; Kamasak, Mustafa; Akan, AydinIn this work, algebraic reconstruction technique (ART) is extended by using non-local means (NLM) and total variation (TV) for reduction of artifacts that are due to insufficient projection data. TV and NLM algorithms use different image models and their application in tandem becomes a powerful denoising method that reduces erroneous variations in the image while preserving edges and details. Simulations were performed on a widely used 2D Shepp-Logan phantom to demonstrate performance of the introduced method (ART + TV) NLM and compare it to TV based ART (ART + TV) and ART. The results indicate that (ART + TV) NLM achieves better reconstructions compared to (ART + TV) and ART.Conference Object Citation - WoS: 1Citation - Scopus: 3Emotional State Analysis From EEG Signals via Noise-Assisted Multivariate Empirical Mode Decomposition Method(IEEE, 2017) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentEmotional state analysis is an interdisciplinary arena because of the many parameters that encompass the complex neural structure and electrical signals of the brain and in terms of emotional state differences. In recent years, emotional state data have been examined by using data-driven methods such as Empirical Mode Decomposition as well as classical time-frequency methods. Although Empirical Mode Decomposition has many advantages, it has disadvantages such as being designed for univariate data, prone to mode mixing, and providing signal via a sufficient number of the local extrema. To overcome these disadvantages, in this study, the Noise-Assisted Multivariate Empirical Mode Decomposition has been shown to classify the emotional state using electroencephalographic signals.Conference Object Citation - Scopus: 5Emotion Detection Using Multivariate Synchrosqueezing Transform via 2D Circumplex Model(Institute of Electrical and Electronics Engineers Inc., 2018) Ozel, Pinar; Akan, Aydin; Yilmaz, Bulent; Özel, Pınar; Akan, Aydin I.; Yilmaz, BulentEmotion detection by utilizing signal processing methods is a challenging area. An open issue in emotional modeling is to obtain an optimum feature set to use for the classification process. This study proposes an approach for emotional state classification by the investigation of EEG signals via multivariate synchrosqueezing transform (MSST). MSST is a post-processing technique to compose a localized time-frequency representation yielding multivariate syncyrosqueezing coefficients. After obtaining these coefficients from EEG signals for 18 subjects from DEAP dataset, coefficients and self-assessment-mannequins (SAM) labels of those subjects are used for emotional state classification by using support vector machines (SVM) nearest neighbor, decision tree, and ensemble methods. The accuracy rate is 70.6% for high valence high arousal (HVHA), 75.4% for low valence high arousal (LVHA), 77.8% for high valence low arousal (HVLA), and 77.2% for low valence low arousal (LVLA) cases using SVM. © 2019 Elsevier B.V., All rights reserved.