Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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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: 7Citation - Scopus: 9Split-Attention Effects in Multimedia Learning Environments: Eye-Tracking and EEG Analysis(Springer, 2022-02-02) Mutlu-Bayraktar, Duygu; Ozel, Pinar; Altindis, Fatih; Yilmaz, BulentThis study aimed to evaluate the split-attention effect in multimedia learning environments via objective measurements as EEG and eye-tracking. Two different multimedia learning environments in a focused (integrated) and split-attention (separated) format were designed. The experimental design method was used. The participants consisted of 44 students divided into two groups for focused attention and split-attention. There were significant differences between the fixation, brain wave, and retention performance of the two groups. Fixations of the split-attention group were higher than the focused attention group. A significant difference was found in the focused attention group in the alpha brain wave in the frontal region for intra-group comparisons and in the split-attention group in the beta brain wave in the frontal area for the inter-group comparison. The retention performance of the focused attention group was higher than the split-attention group. Accordingly, more cognitive activity emerged in environments where the text was not integrated into the picture. Additionally, the narration of text instead of printed text is effective for focusing attention. To prevent the emergence of a split-attention effect, the text should be integrated into the picture in designs. Due to the split-attention effect, the eye-tracking and EEG data were different between the groups.Article Citation - WoS: 14Citation - Scopus: 15Relationship Between Objective and Subjective Cognitive Load Measurements in Multimedia Learning(Routledge Journals, Taylor & Francis Ltd, 2020-11-15) Mutlu-Bayraktar, Duygu; Ozel, Pinar; Altindis, Fatih; Yilmaz, BulentThe aim of this study is to compare subjective and objective cognitive load measurements in a multimedia learning environment. For this purpose, 20 university students studied in multimedia environments designed by researchers during which eye movements and multichannel electroencephalography (EEG) signals were recorded. Self-report ratings were obtained at the end of the experiment, and retention performances of the students were measured. After the data were collected, Pearson Correlation analysis was applied. According to the results, significant relationship between the number of fixations and EEG frequency band powers was found. In addition, there was a negative relationship between retention performance and number of fixations. Moreover, a negative relationship was found between retention performance and self-reported measurements.