WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Now showing 1 - 6 of 6
  • Conference Object
    Citation - Scopus: 2
    Beyin Dalgalari ve Baş Hareketiyle Gerçek Zamanli Robotik Araba Kontrolü
    (Institute of Electrical and Electronics Engineers Inc., 2018-11) Oztürk, Nedime; Yilmaz, Bulent; Onver, Ahmet Yasin
    Emotiv Epoc Headset is a portable and low-cost device. In this study, Emotiv Epoc headset was used in order to obtain real-time gyro and EEG signals. The aim of this study was to control a robotic car in real-time by using head movement and opening and closing of the eyes. The maximum and minimum amplitude of the gyro signal, and the ratios of the beta waves of O1 and O2 channel to alpha waves of the same channels were used as threshold values. These threshold values were used to determine the direction of the robotic car. Because of its low-cost and easy implementation, Arduino Uno was used to manage the robotic car. This study has shown that brain waves and head movements can control a device in real time. This system has the potential to be used in neurofeedback and brain-computer interface applications. © 2019 Elsevier B.V., All rights reserved.
  • Conference Object
    Citation - Scopus: 3
    İki Durumlu Bir Beyin Bilgisayar Arayüzünde Özellik Çıkarımı ve Sınıflandırma
    (Institute of Electrical and Electronics Engineers Inc., 2016-10) Altindis, Fatih; Yilmaz, Bulent
    Brain Computer Interface (BCI) technology is used to help patients who do not have control over motor neurons such as ALS or paralyzed patients, to communicate with outer world. This work aims to classify motor imageries using real-time EEG dataset, which was published by Graz University, Austria. The dataset consists of two-channel EEG signals of right-hand movement imagery and left-hand movement imagery of 8 subjects. There are a total of 120 motor imagery trials (60 left and 60 right) EEG signals recorded from each subject. EEG signals are filtered and feature vectors were extracted that consist of 24, 32 and 40 relative band power values (RBPV). In this work, feature vectors classified by three different methods, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machines (SVM). Results show that best performance was achieved by 24 RBPV feature vector and LDA classification method. © 2017 Elsevier B.V., All rights reserved.
  • Conference Object
    Citation - Scopus: 2
    Emotion Recognition Classification in EEG Signals Using Multivariate Synchrosqueezing Transform
    (IEEE, 2017-10) Ozel, Pinar; Akan, Aydin; Yilmaz, Bulent
    Electrophysiological data processing can take place both in time and in frequency domains as well as in the joint time-frequency domain. Short Time Fourier Transform and Wavelet Transform are commonly used time-frequency analysis methods. The limitations of these methods initiated the use of methods such as synchrosqueezing and multivariate synchrosqueezing methods. In our proposed method 88.9%, 77.8%, 80.6% accuracy rates were obtained respectively for the valence, activation and dominance parameters using and multivariate synchrosqueezing methods and support vector machines(SVM) which yields better results than most of the other methods mentioned in the literature.
  • Conference Object
    Citation - Scopus: 2
    Emotional State Sensing by Using Hybrid Multivariate Empirical Mode Decomposition and Synchrosqueezing Transform
    (IEEE, 2018-11) Ozel, Pinar; Akan, Aydin; Yilmaz, Bulent
    In recent years, utilizing Hilbert-based time frequency methods in emotional state sensing research attracted attention in the brain computer interfaces. Primarily, Hilbert Transform-based empirical mode decomposition (EMD) was found to be suitable for emotional state modeling studies. In more recent studies, models of emotional state recognition were proposed in which the classification was implemented by using the features obtained after applying the time, frequency, and time frequency domain methods to intrinsic mode functions achieved by operating EMD. In this study, an analysis of emotional state recognition is proposed by using the features of the synchrosqueezing coefficients obtained in the classification process after applying the Synchrosqueezing Transform to intrinsic mode functions achieved by using Multivariate EMD. As a result, EEG data available in the DEAP database were categorized as low and high for valence, activation, and dominance dimensions, and 4 different classifiers were utilized in the classification process. The most satisfying ratios of valence, activation and dominance were attained 76%, 68%, and 68% respectively.
  • Conference Object
    Emotion Elicitation Analysis in Multi-Channel EEG Signals Using Multivariate Empirical Mode Decomposition and Discrete Wavelet Transform
    (IEEE, 2017-10) Ozel, Pinar; Akan, Aydin; Yilmaz, Bulent
    In recent years, wavelet-based, Fourier-based and Hilbert-based time-frequency methods attracted attention in emotion state classification studies in human machine interaction. In particular, the Hilbert-based Empirical Mode Decomposition and Wavelet-based Discrete Wavelet Transform have found applications in emotional state analysis. In this study, a model of emotional elicitation is proposed in which the classification is made by using the features of the wavelet coefficients obtained after applying the Discrete Wavelet Transform to IMFs achieved by using Multivariate Empirical Mode Decomposition. Accordingly, EEG data available in the DEAP database were classified as low / high for valence, activation, and dominance dimensions, and 4 different classifiers were used in the classification phase. The best ratios of valence, activation and dominance were obtained ideally 70.1%, 58.8%, 60.3% respectively.
  • Conference Object
    Citation - Scopus: 2
    Beyin Bilgisayar Arayüzü Uygulamalari için Dinlenme, Harekete Niyet ve Hareket Ayırma
    (Institute of Electrical and Electronics Engineers Inc., 2018-11) Oztürk, Nedime; Yilmaz, Bulent
    Brain-computer interface (BCI) is a system that provides a means to control prosthesis, wheelchair, or similar devices using brain waves without direct motor nervous system involvement. For this purpose, brain waves obtained from multiple electrodes placed on the scalp (EEG, Electroencephalogram) are used. Emotiv Epoc used to obtain EEG signals is a low-cost device and has real-time applications. The aim of this study is the detection of rest, imagination and real movement using EEG signals obtained by Emotiv Epoc headset. As a result, As a result, the data obtained from 39 trials from a female subject were classified resting, motion imagination and movement, according to 97.4% accuracy by using the statistical features of distortion, logarithm energy entropy, energy, Shannon entropy and kurtosis. In this study, it has been shown that this system can be remarkably successful for BCI applications. © 2019 Elsevier B.V., All rights reserved.