WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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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.Article Citation - WoS: 3Citation - Scopus: 3Distinguishing Resting State From Motor Imagery Swallowing Using EEG and Deep Learning Models(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Aslan, Sevgi Gokce; Yilmaz, BulentThe primary aim of this study was to assess the classification performance of deep learning models in distinguishing between resting state and motor imagery swallowing, utilizing various preprocessing and data visualization techniques applied to electroencephalography (EEG) data. In this study, we performed experiments using four distinct paradigms such as natural swallowing, induced saliva swallowing, induced water swallowing, and induced tongue protrusion on 30 right-handed individuals (aged 18 to 56). We utilized a 16-channel wearable EEG headset. We thoroughly investigated the impact of different preprocessing methods (Independent Component Analysis, Empirical Mode Decomposition, bandpass filtering) and visualization techniques (spectrograms, scalograms) on the classification performance of multichannel EEG signals. Additionally, we explored the utilization and potential contributions of deep learning models, particularly Convolutional Neural Networks (CNNs), in EEG-based classification processes. The novelty of this study lies in its comprehensive examination of the potential of deep learning models, specifically in distinguishing between resting state and motor imagery swallowing processes, using a diverse combination of EEG signal preprocessing and visualization techniques. The results showed that it was possible to distinguish the resting state from the imagination of swallowing with 89.8% accuracy, especially using continuous wavelet transform (CWT) based scalograms. The findings of this study may provide significant contributions to the development of effective methods for the rehabilitation and treatment of swallowing difficulties based on motor imagery-based brain computer interfaces.