Reflection Emotions Based on Different Stories onto EEG Signal

23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEYWOS: 000380500900639Objective of this study was to investigate reflection of emotions based on different stories onto EEG. EEG data that used in this study have been acquired from database University of California San Diego. EEG signal was filtered to frequency range that contain important sub-bands of EEG by using discrete wavelet transform to analyze reflection of emotion based on different stories onto EEG. Power spectrum estimation of EEG that filtered to related frequancy range was obtained by using Burg method. Diffrerent power spectrum densities onto EEG signal were obtained for each different emotions based on different stories. There are similar studies in literature and results that were obtained in this study support the similar studies.Dept Comp Engn & Elect & Elect Engn, Elect & Elect Engn, Bilkent Uni

Automatic Detection of Emotional State from EEG Signal by Gamma Coherence Approach

Innovations in Intelligent Systems and Applications Conference (ASYU) -- OCT 04-06, 2018 -- Adana, TURKEYWOS: 000455592800006Electroencephalogram coherence analysis is an important measure to help us to assess functional cortical connections and to learn about regional cortical synchronization. In this study, it was aimed to automatically detect emotions related to audio-visual stimuli by electroencephalogram coherence approach. First, the synchronizations of EEG recorded from different regions of the scalp have been analyzed with each other. Coherence analysis was performed for the gamma band of the electroencephalogram signals. Electrode pairs were identified in which the changing emotional state can be observed clearly. The coherence features extracted from the electrode pairs were given to input of the classifier algorithm. The average classification accuracy for the four different participants was obtained as 83.5%.CUKUROVA Univ, Yildiz Tech Univ, IEEE Turkey Sect, Cukurova Univ Comp Eng Dep

Determination Of Changes in Frequencies of EEG Signal in Eyes Open/Closed Duration

23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEYWOS: 000380500900638In this study, the changes of the power spectral density (PSD) in the EEG data during eyes-closed and eyes-open states were analyzed. In the analysis, the interval of dominant frequencies was roughly determined with different approaches. The EEG signal is separated into sub bands with wavelet transform (WT). The Welch method which is the one of the classical methods was used for PSD prediction and the Burg and Yule-Walker parametric methods were used also for PSD prediction of the EEG signal. It was observed that the alpha rhythm is dominant band in the eyes closed state compared to eyes open state.Dept Comp Engn & Elect & Elect Engn, Elect & Elect Engn, Bilkent Uni

MENTAL ACTIVITY DETECTION FROM EEG RECORDS USING LOCAL BINARY PATTERN METHOD

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEYWOS: 000426868700111Electroencephalogram signals are widely used in the detection of different activities but not in the desired level. In this study with this motivation, it is aimed to obtain the attributes by using the Local Bilinear Pattern (LBP) method of EEG records for various mental activities and to classify these features by k-Nearest Neighbor (k-NN) method. The binary classification performance of these EEG records containing 5 mental tasks was evaluated. In addition, in order to evaluate classification performance, confusion matrix was used as model performance criterion. In the study, the average of the classification performance of all participants was found as 87.38%. As a model performance criterion from the participants' classification of mental activity, accuracy was 85.03%, precision was 85.40% and sensitivity was 85.47%. So, as a result the obtained results support the literature and the applicability of the LBP method for EEG markings has been confirmed.IEEE Turkey Sect, Anatolian Sc

Familiarity Effect of Emotional Stimuli onto EEG Signals

Medical Technologies National Conference (TIPTEKNO) -- OCT 27-29, 2016 -- Antalya, TURKEYWOS: 000455003600058The aim of this study was to investigate the familiarity effect of emotional stimuli onto EEG signal. Familiar and non familiar stimuli were determined according to participants' rating and EEG segments related to familiar and non familiar stimuli were analyzed. Discrete wavelet transform (DWT) was used as filter to get the interested frequency range of EEG signals. Power spectral density (PSD) of filtered EEG signals was obtained by using Welch method. The power spectrum of EEG signals was considered as familiarity effects of emotional stimulus. As a conclusion, it was observed that different states of familiarities related to emotional stimulus cause different values of PSD over EEG signals

Classification of EEG Records for the Cursor Movement with the Convolutional Neural Network

Innovations in Intelligent Systems and Applications Conference (ASYU) -- OCT 04-06, 2018 -- Adana, TURKEYWOS: 000455592800035Nowadays, very successful results are obtained with deep learning architectures which can be applied to many fields. Because of the high performances it provides in many areas, deep learning has come to a central position in machine learning and pattern recognition. In this study, electroencephalogram (EEG) signals related to up and down cursor movements were represented as image pattern by using obtained approximation coefficients after wavelet transform. The Obtained image patterns were classified by applying Convolutional Neural Network. In this study, EEG records related to cursor movements were classified and classification accuracy was obtained as 88.13%.CUKUROVA Univ, Yildiz Tech Univ, IEEE Turkey Sect, Cukurova Univ Comp Eng Dep

The Comparison of Wavelet and Empirical Mode Decomposition Method in Prediction of Sleep Stages from EEG Signals

The aim of this study was to detect sleep stages of human by using EEG signals. In accordance with this purpose, discrete wavelet transforms (DWT) and empirical mode decomposition (EMD) were separately used for feature extraction. Subcomponents of EEG signals obtained by the two methods were assumed as feature vectors. Statistical parameters were used to reduce dimension of feature vectors. The same statistical parameters were used to compare performance of methods related to DWT and EMD. K nearest neighborhood (kNN) algorithm was used in classification final feature vectors that obtained EEG segments related to different sleep stages. The classification accuracies for feature vectors based on DWT and EMD were obtained as 100% and 88.13%, respectively.IEEE Turkey Sect, Anatolian Sci2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKE