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    A novel system for automatic removal of ocular artefacts in EEG by using outlier detection methods and independent component analysis
    (Pergamon-Elsevier Science Ltd, 2017) Cinar, Salim; Acir, Nurettin
    ElectroEncephaloGram (EEG) gives information about the electrical characteristics of the brain. EEG can be used for various applications, such as diagnosis of diseases, neuroscience and Brain Computer Interface (BCI). Several artefacts sources can disturb the brain signals in EEG measurements. The signals caused by eye movements are the most important sources of artefacts that must be removed in order to obtain a clean EEG signal. During the removal of Ocular Artefacts (OAs), the preserve of the original EEG signal is one of the most important points to be taken into account. An ElectroOculoGram (EOG) reference signal is needed in order to remove OAs in some methods. However, long-term EOG measurements can disturb a subject. In this paper, a novel robust method is proposed in order to remove OAs automatically from EEG without EOG reference signal by combining Outlier Detection and Independent Component Analysis (OD-ICA). The OD-ICA method searches OA patterns in all components instead of a single component. Moreover, OD-ICA removes only OA patterns and preserves meaningful EEG signal. In this method, user intervention is not needed. These advantages make the method robust. The OD-ICA is tested on two real datasets. Relative Error (RE), Correlation Coefficient (CorrCoeff) and percentage of finding OA pattern are used for the performance test. Furthermore, three different methods are used as Outlier Detection (OD) methods. These are the Chauvenet Criterion, the Peirce's Criterion and the Adjusted Box Plot. The performance analysis is made between our proposed method and the method of zeroing the component with artefact. The experiment results show that the proposed OD-ICA method effectively removes OAs from EEG signals and is also successful in preserving the meaningful EEG signals during the removal of OAs. (C) 2016 Elsevier Ltd. All rights reserved.
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    Automatic detection of kidneys on abdominal CT images using Aggregate Channel Features
    (Institute of Electrical and Electronics Engineers Inc., 2022) Karaman, Merve; Cinar, Salim
    Accurate detection of kidney regions in abdominal CT images makes it easier to detect formations such as cysts, lesions, and stones in the kidneys. In this study, the Aggregate Channel Features (ACF) algorithm, which is a machine learning method, is used for automatic detection of the kidneys. Negative samples are automatically taken from the images during the learning process. The ACF obtained are formed alternately and repeatedly for N steps using the AdaBoost classifier. At each step negative samples are removed and collected with the previous ones. The confusion matrix and k-fold cross-correlation methods are used to test the performance of the study. The data set fragmented according to k-fold is trained according to the location information of the labeled objects using the ACF. Recall, precision, and F1 scores gleaned from the confusion matrix are used in performance analysis. The results show that the proposed method can successfully detect kidney regions. © 2022 IEEE.
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    Automatic Removal of Ocular Artefacts In EEG Signal by Using Independent Component Analysis and Chauvenet Criterion
    (IEEE, 2015) Cinar, Salim; Acir, Nurettin
    Eye movements (saccade, blink and cause artefacts in Electroencephalogram recordings. The ocular artefact can distort the EEG signals. Removal of ocular artefact is important issue in EEG signal analysis. The main task of artefact removal algorithms is to obtain cleaned EEG without losing meaningful EEG signal. The main focus of this work is to remove ocular artefact automatically by using Independent Component Analysis and Chauvenet criterion. The method is tested on real dataset. Relative error and Correlation coefficient are used for the performance test. The performance of the proposed method was Relative error= 0.273 +/- 0.148, Correlation coefficient= 0.943 +/- 0.042 in the dataset. The results show that the porposed method effectively removes ocular artefacts in EEG.
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    Comparison of Split Complex-Valued Metaheuristic Optimization Algorithms for System Identification Problem
    (IEEE, 2018) Menguc, Engin Cemal; Peker, Murat; Cinar, Salim
    Since some of the real world problems include phase and amplitude information, complex modeling is more suitable. In this study, the well-used particle swarm optimization, simulated annealing and genetic algorithm are designed in a split form in order to process complex-valued signals. The performances of the algorithms are comparatively tested on two different system identification problems for different noise levels. Simulation results show that the split complex-valued metaheuristic algorithms produce results which are almost close to the weights of both unknown systems.
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    Design of an automatic hybrid system for removal of eye-blink artifacts from EEG recordings
    (Elsevier Sci Ltd, 2021) Cinar, Salim
    Electroencephalography (EEG) signals are frequently used in several areas, such as diagnosis of diseases and BCI applications. It is important to remove noise sources for applications using EEG. This paper introduces a hybrid system to automatically remove eye-blink artifacts from the EEG by combining several methods, such as Independent Component Analysis (ICA), Kurtosis, K-means, Modified Z-Score (MZS) and Adaptive Noise Canceller (ANC). In the proposed method, all EEG recordings are first decomposed, and then the components related to the eye-blink artifacts are detected using Kurtosis and K-means. The MZS is used to detect regions of only eye-blink artifacts in the independent component. The classical Least Mean Squares (LMS) and Normalized LMS (NLMS) algorithms are used in the proposed ANC system. To comprehensively test the performance of the proposed method, simulated and real-world EEG datasets are used. The proposed system is compared with ANC systems having different reference inputs, Zeroing-ICA, and OD-ICA. In the simulated EEG dataset, the obtained overall RE, CC, SAR, SNR, Sensitivity, Specificity, and Area Under Curve (AUC) values are 0.1505, 0.9875, 1.3863, 2.7708, 100%, 93.8%, and 0.9380, respectively. In the real-world dataset, the obtained overall RE, CC, SAR, and SNR values are 0.0252, 0.9916, 2.8439, and 5.6944, respectively. The results suggest that the proposed method exhibits better performance concerning the given criteria. The proposed system does not require an external electrode for measuring eye-blink artifacts. This distinct advantage provided ensures a comfortable measurement for patients during long-term EEG recordings.
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    Segmentation of MR Images by Using Grow and Learn Network on FPGAs
    (IEEE, 2013) Cinar, Salim; Kurnaz, Mehmet Nadir
    Image segmentation is one of the mostly used procedures in the medical image processing applications. Due to the high resolution characteristics of the medical images and a large amount of computational load in mathematical methods, medical image segmentation process has an excessive computational complexity. Recently, FPGA implementation has been applied in many areas due to its parallel processing capability. In this study, neighbor-pixel-intensity based method for feature extraction and Grow and Learn (GAL) network for segmentation process are proposed. The proposed method is comparatively examined on both PC and FPGA platforms.
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    Segmentation of MR images by using grow and learn network on FPGAs
    (2013) Cinar, Salim; Kurnaz, Mehmet Nadir
    Image segmentation is one of the mostly used procedures in the medical image processing applications. Due to the high resolution characteristics of the medical images and a large amount of computational load in mathematical methods, medical image segmentation process has an excessive computational complexity. Recently, FPGA implementation has been applied in many areas due to its parallel processing capability. In this study, neighbor-pixel-intensity based method for feature extraction and Grow and Learn (GAL) network for segmentation process are proposed. The proposed method is comparatively examined on both PC and FPGA platforms. © 2013 IEEE.