Yazar "Yildirim, Esen" seçeneğine göre listele

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    A Channel Selection Method for Emotion Recognition From EEG Based on Swarm-Intelligence Algorithms
    (IEEE-Inst Electrical Electronics Engineers Inc, 2021) Yildirim, Esen; Kaya, Yasin; Kılıç, Fatih
    Increasing demand for human-computer interaction applications has escalated the need for automatic emotion recognition as emotions are essential for natural communication. There are various information sources that can be used for recognizing emotions, such as speech, facial expressions, body movements, and physiological signals. Among those physiological signals are more reliable for better affective communication with machines since they are almost impossible to control. Therefore, automatic emotion recognition from EEG signals has been a topic intensely investigated. Emotions are experiences that arise various cognitive functions observed in different frequency bands involving multiple brain areas and recognition from EEG with high accuracies is only possible with a large number of features extracted from the whole brain in various bands. Emotion regulation also requires integration of cognitive functions and thus functional connectivity between regions should also be considered. In this paper, we extract 736 features based on spectral power and phase-locking values. We particularly focus on finding salient features for emotion recognition using swarm-intelligence (SI) algorithms. We applied well-known classification algorithms for recognizing positive and negative emotions using the feature sets that are selected by these algorithms. Besides, features that are selected by all of them commonly are used as a new feature set. We report accuracies between 56.27% and 60.29% on the average; noting that by decreasing the feature size by 87.17% (from 736 to 94.40) an average accuracy of 60.01 +/- 8.93 was obtained with the random forest classifier. We also highlight the efficient electrode locations for emotion recognition. As a result, we define 11 channels as dominant and promising classification results are obtained.
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    A Real-Time Parallel Image Processing Approach on Regular PCs with Multi-Core CPUs
    (Kaunas Univ Technology, 2017) Atasoy, Huseyin; Yildirim, Esen; Yildirim, Serdar; Kutlu, Yakup
    In this paper a parallel image processing and frame rate stabilization approach is proposed. This approach works on a regular PC with a multi-core CPU. It is implemented under. NET Framework and tested on Microsoft Windows 7 operating system, performing several experiments. It is also applied to a face recognition application to increase its image processing performance successfully. Results show that, handled workload when 4 physical cores are used is approximately 5.25 times the workload handled with one core. It is also shown that the approach successfully distributes the workload on CPU cores and produces output at a stable frame rate under both steady and unsteady workloads. This approach can be used for various signal processing or multimedia applications to parallelize their tasks to increase the performance on multi-core CPUs.
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    Analysis of functional brain connections for positive-negative emotions using phase locking value
    (Springer, 2017) Dasdemir, Yasar; Yildirim, Esen; Yildirim, Serdar
    In this study, we investigate the brain networks during positive and negative emotions for different types of stimulus (audio only, video only and audio + video) in , and bands in terms of phase locking value, a nonlinear method to study functional connectivity. Results show notable hemispheric lateralization as phase synchronization values between channels are significant and high in right hemisphere for all emotions. Left frontal electrodes are also found to have control over emotion in terms of functional connectivity. Besides significant inter-hemisphere phase locking values are observed between left and right frontal regions, specifically between left anterior frontal and right mid-frontal, inferior-frontal and anterior frontal regions; and also between left and right mid frontal regions. ANOVA analysis for stimulus types show that stimulus types are not separable for emotions having high valence. PLV values are significantly different only for negative emotions or neutral emotions between audio only/video only and audio only/audio + video stimuli. Finding no significant difference between video only and audio + video stimuli is interesting and might be interpreted as that video content is the most effective part of a stimulus.
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    Channel Selection from EEG Signals and Application of Support Vector Machine on EEG Data
    (IEEE, 2017) Arslan, Mustafa Turan; Eraldemir, Server Goksel; Yildirim, Esen
    In this study, EEG data recorded during mental arithmetic operations and silent reading were analyzed by discrete wavelet transform and feature vectors were obtained. The obtained feature vectors are classified by Support Vector Machines (SVM). Results are given for 26 channels, all recorded channels, and for 10 most effective channels. Correlation based feature selection based algorithm is used for choosing the most effective channels. Decreasing the number of channels without compromising the accuracy, is an important issue for real time applications for which a short analysis time is crucial. In this study, mental arithmetic and silent reading tasks are classified with an accuracy of 90.71%, a precision rate of 91.03% and F-measure rate of 90.63% on the average using 26 channels, whereas the accuracy, precision and F-measure were 90.44%, 90.61% and 90.08, respectively which were comparable to that of obtained using all channels, for reduced number of channels.
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    Channel selection from EEG signals and application of support vector machine on EEG data
    (Institute of Electrical and Electronics Engineers Inc., 2017) Arslan, Mustafa Turan; Eraldemir, Server Göksel; Yildirim, Esen
    In this study, EEG data recorded during mental arithmetic operations and silent reading were analyzed by discrete wavelet transform and feature vectors were obtained. The obtained feature vectors are classified by Support Vector Machines (SVM). Results are given for 26 channels, all recorded channels, and for 10 most effective channels. Correlation based feature selection based algorithm is used for choosing the most effective channels. Decreasing the number of channels without compromising the accuracy, is an important issue for real time applications for which a short analysis time is crucial. In this study, mental arithmetic and silent reading tasks are classified with an accuracy of 90.71%, a precision rate of 91.03% and F-measure rate of 90.63% on the average using 26 channels, whereas the accuracy, precision and F-measure were 90.44%, 90.61% and 90.08, respectively which were comparable to that of obtained using all channels, for reduced number of channels. © 2017 IEEE.
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    Classification of Intensive-less Intensive and Related-Unrelated Tasks
    (Prof.Dr. İskender AKKURT, 2024) Arslan, Mustafa Turan; Yildirim, Esen
    This study investigates the classification of electrical brain activity during intensive-less intensive and related-unrelated tasks. EEG signals were collected from 20 physically and mentally healthy university students (15 males, 5 females) residing in Adana and Hatay, Turkey, through 14 channels. Continuous Wavelet Transform analysis was applied for feature extraction. Subsequently, subject-dependent and subject-independent classifications were performed using the k-nearest Neighbour algorithm. In subjectdependent classification, the accuracy range for intensive-less intensive tasks varied between 77.6% and 89.8%, while the range for related-unrelated tasks was between 73.2% and 88%. Subject-independent classification yielded an accuracy of 79.2% for intensive-less intensive tasks and 77.5% for related unrelated tasks. © IJCESEN
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    LU triangularization extreme learning machine in EEG cognitive task classification
    (Springer London Ltd, 2019) Kutlu, Yakup; Yayik, Apdullah; Yildirim, Esen; Yildirim, Serdar
    Electroencephalography (EEG) has been used as a promising tool for investigation of brain activity during cognitive processes. The aim of this study is to reveal whether EEG signals can be used for classifying cognitive processes: arithmetic tasks and text reading. A recently introduced EEG database, which is constructed from 18 healthy subjects during a slide show including 60 slides of simple arithmetic tasks and easily readable texts, is used for this purpose. Multi-order difference plot-based time-domain attributes, number of values in specified regions after scattering the sequential difference values with several degrees, are extracted. For classification, improved extreme learning machine (ELM) scheme, namely luELM, by the use of lower-upper triangularization method instead of singular value decomposition which has disadvantages when used with huge data is proposed. As a result, higher accuracy results are achieved with reduced training time for proposed luELM classifier than traditional ELM classifier for both subject-dependent and subject-independent analysis.
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    Ultra-Wideband (UWB) characteristic estimation of elliptic patch antenna based on machine learning techniques
    (Walter De Gruyter Gmbh, 2020) Gencoglan, Duygu Nazan; Arslan, Mustafa Turan; Colak, Sule; Yildirim, Esen
    In this study, estimation of Ultra-Wideband (UWB) characteristics of microstrip elliptic patch antenna is investigated by means of k-nearest neighborhood algorithm. A total of 16,940 antennas are simulated by changing antenna dimensions and substrate material. Antennas are examined by observing Return Loss and Voltage Standing Wave Ratio (VSWR) characteristics. In the study, classification of antennas in terms of having UWB characteristics results in accuracies higher than 97%. Additionally, Consistency based Feature Selection method is applied to eliminate redundant and irrelevant features. This method yields that substrate material does not affect the UWB characteristics of the antenna. Classification process is repeated for the reduced feature set, reaching to 97.44% accuracy rate. This result is validated by 854 antennas, which are not included in the original antenna set. Antennas are designed for seven different substrate materials keeping all other parameters constant. Computer Simulation Technology Microwave Studio (CST MWS) is used for the design and simulation of the antennas.