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Dementia is one of the most common neurological disorders causing defection of cognitive functions, and seriously affects the quality of life. In this study, various methods have been proposed for the detection and follow-up of Alzheimer’s dementia (AD) with advanced signal processing methods by using electroencephalography (EEG) signals. Signal decomposition-based approaches such as empirical mode decomposition (EMD), ensemble EMD (EEMD), and discrete wavelet transform (DWT) are presented to classify EEG segments of control subjects (CSs) and AD patients. Intrinsic mode functions (IMFs) are obtained from the signals using the EMD and EEMD methods, and the IMFs showing the most significant differences between the two groups are selected by applying previously suggested selection procedures. Five-time-domain and 5-spectral-domain features are calculated using selected IMFs, and five detail and approximation coefficients of DWT. Signal decomposition processes are conducted for both 1 min and 5 s EEG segment durations. For the 1 min segment duration, all the proposed approaches yield prominent classification performances. While the highest classification accuracies are obtained using EMD (91.8%) and EEMD (94.1%) approaches from the temporal/right brain cluster, the highest classification accuracy for the DWT (95.2%) approach is obtained from the temporal/left brain cluster for 1 min segment duration.

Empirical Mode Decomposition (EMD) provides an adaptive signal processing tool, and its multivariate extension is useful to model multichannel signals. Recently, EMD and multivariate EMD have succe...

Dynamic mode decomposition (DMD) is a new matrix decomposition method proposed as an iterative solution to problems in fluid flow analysis. Recently, DMD algorithm has successfully been applied to the analysis of non-stationary signals such as neural recordings. In this study, we propose single-channel, and multi-channel EEG based DMD approaches for the analysis of epileptic EEG signals. We investigate the possibility of utilizing the “DMD Spectrum” for the classification of pre-seizure and seizure EEG segments. We introduce higher-order DMD spectral moments and DMD sub-band powers, and extract them as features for the classification of epileptic EEG signals. Experiments are conducted on multi-channel EEG signals collected from 16 epilepsy patients. Single-channel, and multi-channel EEG based DMD approaches have been tested on epileptic EEG data recorded from only right, only left, and both brain hemisphere channels. Performance of various classifiers using the proposed DMD-Spectral based features are compared with that of traditional spectral features. Experimental results reveal that the higher order DMD spectral moments and DMD sub-band power features introduced in this study, outperform the analogous spectral features calculated from traditional power spectrum.

In this study, a liking estimation system based on electroencephalogram (EEG) signals is developed for neuromarketing applications. The determination of the degree of appreciation of a product by consumers has become an important research topic using machine learning methods. Biological data is recorded while viewing product pictures or videos, then processed by signal processing methods. In this study, 32 channel EEG signals are recorded from subjects who watched two different car advertisement videos and the liking status is determined. After watching the advertisement videos, the participants were asked to vote for the rating of the different images (front view, dashboard, side view, rear view, taillight, logo and grille) of the products. The signals corresponding to these different video regions from the EEG recordings were segmented and analyzed by the Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD). The statistical features were extracted from Intrinsic Mode Functions (IMF) and the liking status classifications were performed. The classification performance of EMD- and EEMD-based methods are 93.4% and 97.8% respectively on Brand1, and 93.5% and 97.4% respectively on Brand2. In addition, the classification accuracy on both brands combined are 85.1% and 85.7% respectively. The promising results obtained using Support Vector Machines (SVM) show that the proposed EEG-based method may be used in neuromarketing studies.

Background Epilepsy is one of the most common neurological disorders associated with disruption of brain activity. In the classification and detection of epileptic seizures, electroencephalography (EEG) measurements, which record the electrical activities of the brain, are frequently used. Empirical mode decomposition (EMD) and its derivative, ensemble EMD (EEMD) are recently developed methods used to decompose non-stationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs). Our main objective in this study is to present a hybrid IMF selection method combining four different approaches (energy, correlation, power spectral distance, and statistical significance measures), and investigate the effect of selected IMFs extracted by EMD and EEMD on the classification. We have applied the proposed IMF selection approach on the classification of EEG signals recorded from epilepsy patients who are under treatment at our collaborator hospital. Multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then IMF selection was performed. Finally, time- and spectral-domain, and nonlinear features are extracted and feature sets are created for the classification. Results The maximum classification accuracies obtained using various combinations of IMFs were 94.56%, 95.63%, 96.8%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression classifiers, respectively, by using EMD analysis; whereas, the EEMD approach has provided maximum classification accuracies of 96.06%, 97%, 97%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression, respectively. Classification performance with the same features obtained using direct EEG signals instead of the decomposed IMFs was worse than the aforementioned 2 approaches for every combination. Conclusion Simulation results demonstrate that the proposed IMF selection approach affects the classification results. Also, EEMD provides a robust method for feature extraction from EEG signals in order to classify pre-seizure and seizure segments.

Epilepsy is a persistent and recurring neurological condition in a community of brain neurons that results from sudden and abnormal electrical discharges. This paper introduces a new form of assessment and interpretation of the changes in electroencephalography (EEG) recordings from different brain regions in epilepsy disorders based on graph analysis and statistical rescale range analysis. In this study, two different states of epilepsy EEG data (preictal and ictal phases), obtained from 17 subjects (18 channels each), were analyzed by a new method called state transfer network (STN). The analysis performed by STN yields a network metric called motifs, which are averaged over all channels and subjects in terms of their persistence level in the network. The results showed an increase of overall motif persistence during the ictal over the preictal phase, reflecting the synchronization increase during the seizure phase (ictal). An evaluation of intermotif cross-correlation indicated a definite manifestation of such synchronization. Moreover, these findings are compared with several other well-known methods such as synchronization likelihood (SL), visibility graph similarity (VGS), and global field synchronization (GFS). It is hinted that the STN method is in good agreement with approaches in the literature and more efficient. The most significant contribution of this research is introducing a novel nonlinear analysis technique of generalized synchronization. The STN method can be used for classifying epileptic seizures based on the synchronization changes between multichannel data.

This research presents a new method for detecting obsessive–compulsive disorder (OCD) based on time–frequency analysis of multi-channel electroencephalogram (EEG) signals using the multi-variate synchrosqueezing transform (MSST). With the evolution of multi-channel sensor implementations, the employment of multi-channel techniques for the extraction of features arising from multi-channel dependency and mono-channel characteristics has become common. MSST has recently been proposed as a method for modeling the combined oscillatory mechanisms of multi-channel signals. It makes use of the concepts of instantaneous frequency (IF) and bandwidth. Electrophysiological data, like other nonstationary signals, necessitates both joint time–frequency analysis and independent time and frequency domain studies. The usefulness and effectiveness of a multi-variate, wavelet-based synchrosqueezing algorithm paired with a band extraction method are tested using electroencephalography data obtained from OCD patients and control groups in this research. The proposed methodology yields substantial results when analyzing differences between patient and control groups.

The application of EEG-based emotional states is one of the most vital phases in the context of neural response decoding. Emotional response mostly appears in the presence of visual, auditory, tactile, and gustatory arousals. In our work, we use visual stimuli to evaluate the emotional feedback. One of the best performing methods in emotion estimation applications is the common spatial patterns (CSP). We implement CSP method in addition to the conventional Welch power spectral density-based analysis. Experimental results and topographies on the collected EEG data show that the CSP spatial filtering method implies the relationship between EEG bands, EEG channels, neural efficiency and emotional stimuli types.

Major depressive disorder (MDD) is a common mood disorder encountered worldwide. Early diagnosis has great importance to prevent the negative effects on the person. The aim of this study is to develop an objective method to differentiate MDD patients from healthy controls. Electroencephalography (EEG) signals taken from 16 MDD patients and 16 healthy subjects are analyzed according to the regions of the brain, and time-domain, frequency-domain, and nonlinear features were extracted. The feature sets are classified using five different classification algorithms. As a result of the study, a classification accuracy of 89.5% was yielded using the Bagging classifier with 7 features calculated from the central EEG channels.

Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using EEG signals for the detection and prediction of seizures. In this study, a novel method based on Fourier-based Synchrosqueezing Transform (SST), which is a high-resolution time-frequency (TF) representation, and Convolutional Neural Network (CNN) is proposed to detect and predict seizure segments. SST is based on the reassignment of signal components in the TF plane which provides highly localized TF energy distributions. Epileptic seizures cause sudden energy discharges which are well represented in the TF plane by using the SST method. The proposed SST-based CNN method is evaluated using the IKCU dataset we collected, and the publicly available CHB-MIT dataset. Experimental results demonstrate that the proposed approach yields high average segment-based seizure detection precision and accuracy rates for both datasets (IKCU: 98.99% PRE and 99.06% ACC; CHB-MIT: 99.81% PRE and 99.63% ACC). Additionally, SST-based CNN approach provides significantly higher segment-based seizure prediction performance with 98.54% PRE and 97.92% ACC than similar approaches presented in the literature using the CHB-MIT dataset.

Epilepsy is a neurological disease that is very common worldwide. In the literature, patient’s electroencephalography (EEG) signals are frequently used for an epilepsy diagnosis. However, the success of epileptic examination procedures from quantitative EEG signals is limited. In this paper, a high-resolution time-frequency (TF) representation called Synchrosqueezed Transform (SST) is used to classify epileptic EEG signals. The SST matrices of seizure and pre-seizure EEG data of 16 epilepsy patients are calculated. Two approaches based on machine learning and deep learning are proposed to classify pre-seizure and seizure signals. In the machine learning-based approach, the various features like higher-order joint moments are calculated and these features are classified by Support Vector Machine (SVM), k-Nearest Neighbor (kNN) and Naive Bayes (NB) classifiers. In the deep learning-based approach, the SST matrix was recorded as an image and a Convolutional Neural Network (CNN)-based architecture was used to classify these images. Simulation results demonstrate that both approaches achieved promising validation accuracy rates. While the maximum (90.2%) validation accuracy is achieved for the machine learning-based approach, (90.3%) validation accuracy is achieved for the deep learning-based approach.

Epilepsy is a neurological disorder that affects many people all around the world, and its early detection is a topic of research widely studied in signal processing community. In this paper, a new technique that was introduced to solve problems of fluid dynamics called Dynamic Mode Decomposition (DMD), is used to classify seizure and non-seizure epileptic EEG signals. The DMD decomposes a given signal into the intrinsic oscillations called modes which are used to define a DMD spectrum. In the proposed approach, the DMD spectrum is obtained by applying either multi-channel or single-channel based DMD technique. Then, subband and total power features extracted from the DMD spectrum and various classifiers are utilized to classify seizure and non-seizure epileptic EEG segments. Outstanding classification results are achieved by both the single-channel based (96.7%), and the multi-channel based (96%) DMD approaches.

Epilepsy is a type of neurological disorder that causes abnormal brain activities and creates epileptic seizures. Traditionally epileptic seizure prediction is realized with a visual examination of Electroencephalogram (EEG) signals. But this technique needs a long time EEG monitoring. So, the automatic epileptic seizures prediction schemes become a requirement at this point. This study proposes a method to classify epileptic seizures and normal EEG data by utilizing the Intrinsic Time-scale Decomposition (ITD)-based features. The dataset has been supplied from the database of the Epileptology Department of Bonn University. It contains 5 data groups A, B, C, D, E. The study aims to classify healthy and epileptic data, so data of groups A and E are used to perform evaluations of proposed methods. The EEG data are decomposed into Proper Rotation Components (PRCs) by ITD. The feature extraction methods are applied to the first five PRCs of each EEG data from healthy and epileptic individuals. These features are classified using K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naive Bayes, Support Vector Machine (SVM) and Logistic Regression classifiers. The results demonstrated that the epileptic data is differentiated from normal data by applying the nonlinear ITD with outstanding classification performance.

In this study, it is determined individual-based features which are used to estimate emotional negative valence and compared the features effectiveness with different classifiers. Ten movie clips are shown to subjects as an emotional stimuli and EEG recording is recorded synchronously. Emotional valence value is scored in [–7 7] Likert scale by the subjects immediately after video ended. According to lowest and highest valence values, two classes are generated. The data is processed on an individual basis and personal spatial filters is obtained by Independent Component Analysis. After calculating the spectrogram of the spatial filtered data, features are extracted by subtracting amplitudes of 3Hz averaged frequency bands. The result of feature selection, it is observed that features from beta and gamma bands are much more effective. The success rate of the selected features was tested with five classifiers by cross validation, and high performance was obtained from multilayer perceptron classifiers and the instance- based k-nearest neighborhood algorithm (IBk-NN). The average accuracies of IBk-NN and multilayer classifier are achieved 86% ±8 and 83% ±9, respectively.

Epilepsy is one of the neurological diseases that occur incidences worldwide. The electroencephalography (EEG) recording method is the most frequently used clinical practice in the diagnosis and monitoring of epilepsy. Many computer-aided analysis methods have been developed in the literature to facilitate the analysis of long-term EEG signals. In the proposed study, the patient-based seizure detection approach is proposed using a high-resolution time-frequency (TF) representation named Synchrosqueezed Transform (SST) method. The SST of two different data sets called the IKCU data set and CHB-MIT data set are obtained, and Higher-order joint TF(HOJ-TF) based and Gray-level co-occurrence matrix (GLCM) based features are calculated using these SSTs. Using some machine learning methods such as Decision Tree (DT), k-Nearest Neighbor (kNN), and Logistic Regression (LR), classification processes are conducted. High patient-based seizure detection success is achieved for both the IKCU data set (94.25%) and the CHB-MIT data set (95.15%).

In this study, an estimation system based on electroencephalogram (EEG) signals has been developed for use in neuromarketing applications. Determination of the degree of consumer liking a product by processing the biological data (EEG, facial expressions, eye tracking, Galvanic skin response, etc.) recorded while viewing the product images or videos has become an important research topic. In this study, 32-channel EEG signals were recorded from subjects while they watch two different car advertisement videos, and the liking status was determined. After watching the car commercial videos, the subjects were asked to vote on the rating of different images (front view, front console, side view, rear view, rear light, logo and front grill) of the cars. The signals corresponding to these different video regions from the EEG recordings were segmented and analyzed by the Empirical Mode Decomposition (EMD) method. Several statistical features were extracted from the resulting Intrinsic Mode Functions and the liking status classification was performed. Classification results obtained with Support Vector Machines (SVM) classifiers indicate that the proposed EEG-based liking detection method may be used in neuromarketing studies.

In the literature, several signal processing techniques have been used to diagnose epilepsy which is a nervous system disease. However most of these techniques fail to analyse EEG signals which are dynamic and non-linear. In this study, an approach which utilizes a data-driven technique called Dynamic Mode Decomposition (DMD) that was originally developed to be used in fluid mechanics was proposed. Features that were belonged to EEG signals were calculated using DMD method and with the help of different classifiers, classification of the preseizure and seizure EEG signals was performed. Obtained results showed that the proposed method presented an alternative to approaches that are based on Empirical Mode Decomposition and its derivatives.

Emotion recognition is an effective analysis method used to increase the interaction between human-machine interface. EEG based emotion recognition studies based on brain signals are preferred in order to provide healthy results of emotion analysis experiments. In this study, emotion recognition analysis was performed in accordance with dimensional emotion modelling. Data cleaning was performed by applying the necessary filters on the recorded data. The feature vector was then created and the success rate was determined using support vector machines and classification methods such as K-nearest neighbour.

Obsessive–compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements; intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization likelihood, and intrinsic visibility graph similarity that quantifies the synchronization level and complexity in electroencephalography (EEG) signals. This intrinsic synchronization is achieved by utilizing Multivariate Empirical Mode Decomposition (MEMD), a data-driven method that resolves nonlinear and nonstationary data into their intrinsic mode functions. Our intrinsic technique in this study demonstrates that MEMD-based synchronization analysis gives us much more detailed knowledge rather than utilizing the synchronization method alone. Furthermore, the nonlinear synchronization method presents more consistent results considering OCD heterogeneity. Statistical evaluation using sample [Formula: see text]-test and [Formula: see text]-test has shown the significance of such new methodology.

The emotional state of people plays a key role in physiological and behavioral human interaction. Emotional state analysis entails many fields such as neuroscience, cognitive sciences, and biomedical engineering because the parameters of interest contain the complex neuronal activities of the brain. Electroencephalogram (EEG) signals are processed to communicate brain signals with external systems and make predictions over emotional states. This paper proposes a novel method for emotion recognition based on deep convolutional neural networks (CNNs) that are used to classify Valence, Arousal, Dominance, and Liking emotional states. Hence, a novel approach is proposed for emotion recognition with time series of multi-channel EEG signals from a Database for Emotion Analysis and Using Physiological Signals (DEAP). We propose a new approach to emotional state estimation utilizing CNN-based classification of multi-spectral topology images obtained from EEG signals. In contrast to most of the EEG-based approaches that eliminate spatial information of EEG signals, converting EEG signals into a sequence of multi-spectral topology images, temporal, spectral, and spatial information of EEG signals are preserved. The deep recurrent convolutional network is trained to learn important representations from a sequence of three-channel topographical images. We have achieved test accuracy of 90.62% for negative and positive Valence, 86.13% for high and low Arousal, 88.48% for high and low Dominance, and finally 86.23% for like–unlike. The evaluations of this method on emotion recognition problem revealed significant improvements in the classification accuracy when compared with other studies using deep neural networks (DNNs) and one-dimensional CNNs.

This paper explores the data-driven properties of the empirical mode decomposition (EMD) for detection of epileptic seizures. A new method in frequency domain is presented to analyze intrinsic mode functions (IMFs) decomposed by EMD. They are used to determine whether the electroencephalogram (EEG) recordings contain seizure or not. Energy levels of the IMFs are extracted as threshold level to detect the changes caused by seizure activity. A scalar value energy resulting from the energy levels is individually used as an indicator of the epileptic EEG without the requirements of multidimensional feature vector and complex machine learning algorithms. The proposed methods are tested on different EEG recordings to evaluate the effectiveness of the proposed method and yield accuracy rate up to 97.89%.

Emotions play a significant role in daily life by encouraging the individual in the survival, decision making, guessing, and communication processes. Through emotions can be explained with the activation of anatomical structures in certain regions of brain with nervous system the emotions can be understood by electroencephalogram (EEG) signals. In order to recognize emotions, the signal processing techniques were applied to recorded signals using 32-channels EEG device from the subjects during listening audios. The Self-Assessment Manikin (SAM) form was filled by 23 subjects to evaluate their feelings based on three emotion states and recorded their answers by designed Graphical User Interface (GUI) monitored in front of the subjects. In signal processing stage, the EEG signals were segmented into segmented files by cutting stimulus intervals from recorded signal and decomposed to Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition (EMD) method. Then, most meaningful IMFs has been selected by analyzing Power Spectral Density (PSD) to extract statistical and entropy-based features and then, classification algorithm has been applied to obtain feature vector to categorize states of emotion consisting of valence, arousal, dominance dimensions. It is aimed to find most useful selected IMFs, most active channels related to emotion, best suitable features for each dimension of emotion. Finally, the percentage of performance accuracy has been calculated and the best accuracy of 81.74% is found in channels ranged in frontal lobe (1–12) for valence state, 72.15% in channel TP7 for arousal state, 74.57% in channels ranged in frontal lobe (1–12) for dominance state by combining different features.

Empirical mode decomposition (EMD) method is a powerful tool in the analysis of of nonlinear and nonstationary signals. It decomposes signals into a number of amplitude and frequency modulated signals namely intrinsic mode functions (IMFs). However, some of these IMFs represents the original signal better while some of them are useless. IMF selection methods are suggested to determine the IMFs which represents the original signal better than other IMFs. In this study, we analyzed the effect of IMF selection methods in classification performance. We compared power based, correlation based and power spectral density based IMF selection methods in the classification of the electroencephalography (EEG) signals, which are collected from subjects with multiple sclerosis. The EEG signals are classified as the patients are being cognitively impaired or intact. k-nearest neighbors, multilayer perceptron neural networks and random forest classifiers are used in classification. The results show that, effect of IMF selection methods on accuracy is changeable in regard to classifier preference.

Alzheimer's Disease and Dementia are increasing diseases with the aging population. These diseases cause memory loss, impaired attention, impaired problem-solving abilities. Serious games are designed to prevent or slow the progression of these diseases and to reduce the effects of diseases. In this study, a 2-dimensional maze game is designed as a serious game. The purpose of this study is to play the game by using EEG signals recording from the user.

This study focuses on a signal processing method, which qualifies the relation between the emotional stimulation and emotional changes in healthy participants. For this purpose, an emotional EEG-based database was created by using stimuli which represented to the participants by using Nencki Affective Picture System (NAPS) for the scope of this study. Then, signal processing which includes Power Spectral Density (PSD) and a number of classification analysis is investigated. PSD was analyzed for the emotion connectivity between three types of emotional stimuli to the sub frequency band (gamma band) of the collected EEG-based data. In recent times, most of the studies commonly concentrated on the Brain-Computer Interface (BCI) and we aim that this study performed on emotional changes analyses based BCI systems will be estimable work for the researchers working in this scope.

Obsessive Compulsive Disorder (OCD) is one of the most common neuropsychiatric disorder in the community. Several neuroimaging systems shows that OCD causes functional disorders in the frontal lobe. In this study, the effects of the OCD on the frontal part are investigated with Inter-Channels Phase Clustering (ICPC) method. According to the Significant desynchronization was detected in slow EEG bands for 7 electrodes on the frontal lobe. These findings are consistent with the previous results that obtained by other neuroimaging devices. The results are also showed that loss of frontal synchronization cause functional disconnectivity. In addition to this, it can be concluded that OCD may cause many cognitive dysfunctions, such as loss of memory.

Emotion detection is very crucial role on diagnosis of brain disorders and psychological disorders. Electroencephalogram (EEG) is useful tool that obtain complex physiological brain signals from human. In this paper, we proposed a novel approach for emotional state estimation using convolutional neural network (CNN) based deep learning technique from EEG signals. Firstly, we convert 32 lead EEG signals to 2D EEG images with Azimuthal Equidistant Projection (AEP) technique. Then, 2D images that represented measurements of EEG signals sent to CNN based deep neural network for classification. In this study, we have achieved accuracy of 95.96% two classes as negative and positive valence, 96.09% two classes as high and low arousal and 95.90% two classes as high and low arousal dominance.

Epilepsy is a neurological disease that is very common worldwide. Patient’s electroencephalography (EEG) signals are frequently used for the detection of epileptic seizure segments. In this paper, a high-resolution time-frequency (TF) representation called Synchrosqueezing Transform (SST) is used to detect epileptic seizures. Two different EEG data sets, the IKCU data set we collected, and the publicly available CHB-MIT data set are analyzed to test the performance of the proposed model in seizure detection. The SST representations of seizure and nonseizure (pre-seizure or inter-seizure) EEG segments of epilepsy patients are calculated. Various features like higher-order joint TF (HOJ-TF) moments and gray-level co-occurrence matrix (GLCM)-based features are calculated using the SST representation. By using single and ensemble machine learning methods such as k-Nearest Neighbor (kNN), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Boosted Trees (BT), and Subspace kNN (S-kNN), EEG features are classified. The proposed SST-based approach achieved 95.1% ACC, 96.87% PRE, 95.54% REC values for the IKCU data set, and 95.13% ACC, 93.37% PRE, 90.30% REC values for the CHB-MIT data set in seizure detection. Results show that the proposed SST-based method utilizing novel TF features outperforms the short-time Fourier transform (STFT)-based approach, providing over 95% accuracy for most cases, and compares well with the existing methods.

This is the artifact for the paper titled "POKER: Permutation-based SIMD Execution of Intensive Tree Search by Path Encoding" accepted at CGO 2018. This artifact helps reproduce the results presented in Figures 7 - 9 and Tables 2 - 3 in Section 4. For more information on how to use it, please refer to our paper and the README.txt file in this package. Please note that POKER is a work in progress. This artifact is a snapshot of this work and thus is only applicable under the experimental settings described in this paper. Please feel free to contact the authors if you have any questions.

Epilepsy is a neurological disorder of different types characterized by recurrent of seizures which affects people of all ages. This paper presents visibility graph similarity as a nonlinear model to analyze the epilepsy EEG data from different brain region of healthy and patient subjects with epilepsy seizures. All EEG segments are mapped into a corresponding graph to obtain the corresponding degree of sequence for each segment, and then the difference between these degrees is constructed as a similarity between two segments. The results showed that seizure activity presented strongest nonlinear dynamic response in the form of similarity level decreasing from healthy subjects to patients. Results of other sets were found to be in agreement with our results.

This study investigates improved properties of empirical mode decomposition (EMD) for emotion recognition by using electroencephalogram (EEG) signals. The emotion recognition from EEG signals is a difficult study by the reason of nonstationary behavior of the signals. These signals are affected from complicated neural activity of brain. To analyze EEG signals, advanced signal processing techniques are required. In our study, data are collected from one channeled BIOPAC lab system. EEG signals were obtained from visual evoked potentials of 13 female and 13 male volunteers for 12 pleasant and 12 unpleasant pictures. To analyze nonlinear and nonstationary characteristics of EEG signals, an EMD-based method is proposed for emotion recognition. Various time and frequency domain techniques such as power spectral density (PSD), and higher order statistics (HOS) are used to analyze the IMFs extracted by EMD. Support vector machine (SVM), Linear discriminant analysis (LDA), and Naive Bayes classifiers are utilized for the classification of features extracted from the IMFs, and their performances are compared.

The detection of epileptic seizure has a primary role in patient diagnosis with epilepsy. Epilepsy causes sudden and uncontrolled electrical discharges in brain cells. Recordings of the abnormal brain activities are time consuming and outcomes are very subjective, so automated detection systems are highly recommended. In this study, it is aimed to classify EEG signals for the detection of epileptic seizures using intrinsic mode functions (IMF) and feature extraction based on Empirical Mode Decomposition (EMD). These records have been acquired from the database of the Epileptology Department of Bonn University and consisting of 5 marker groups A, B, C, D, E in this study. These records taken from healthy individuals and patients are decomposed into IMFs by EMD method. Feature vectors have been extracted based on Tsallis Entropy, Renyi Entropy, Relative Entropy and Coherence methods. These features are then classified by K-Nearest Neighbors Classification (KNN), Linear Discriminant Analysis (LDA) and Naive Bayes Classification (NBC). Significant differences were determined between healthy and patient EEG data at the end of the study.

EEG is one of the most used devices in brain examinations, provides important information about many neurological diseases. The power of EEG signal's phase synchronization is a parameter that has been commonly used in brain investigations. The Interchannels Phase Clustering (ICPC) method that we propose in our study, is an improved version of the Intertrial Phase Clustering (ICPC) method. Global and regional phase synchronization studies can be done with ICPC method. The method is applied to the signals produced by an EEG simulator and the results are computed with high accuracy. This method can be applied to neuropsychiatric diseases and provide important information on functional connectivity and cognitive functions.

Epilepsy is a chronic neurological disorder affects people of all ages; this paper presents cross-correlation and state transfer network method to analyze synchronization between EEG data-channels from subjects with epilepsy seizures. The datasets are in two phases, preictal and ictal phase. All EEG segments are mapped into a corresponding state network graph to obtain the corresponding motifs and then the cross-correlation is applied to exhibit the synchronization changing during epilepsy seizures. The results showed that ictal phase presented high synchronization between channels, where low synchronization level is observed within preictal phase.

Electroencephalogram (EEG) signals are widely used to investigate the electrical activities in the brain an to get information for these activities. In this work, samples are selected from 3 type (Negative, Neutral, Positive) emotional pictures from ‘Nencki Affective Picture System’ and shown to subjects. EEG signals are collected according to 10–20 international electrode system standards. Collected signals are reduced to 3 type channels (Frontal, Parieto-Occipital, Central). Power Spectral Density (PSD) of the corresponding EEG signals are investigated and separated into EEG frequency bands (Delta, Theta, Alpha, Beta). Welch Spectral Estimation Method is applied into the acquired data and their PSDs are obtained after taking the expectation of their frequencies. Using PSDs, total power is calculated for each frequency band and channel. Then ANOVA analysis is performed and statistical differences are investigated. Our analyses show that statistically significant differences exist between each frequency band and 3 type of channels and among reduced channels themselves.

In this study, our aim is to distinguish pre-seizure and seizure data from epileptic EEG signals using Ensemble Empirical Mode Decomposition (EEMD) and various classifiers. For this purpose, epileptic EEG data from 13 epileptic patients have been recorded using surface electrodes at Izmir Kâtip Celebi University School of Medicine, Neurology Department. First, EEG signals are divided into two parts as pre-seizure and seizure and decomposed into intrinsic mode functions (IMFs) using EEMD. Total power and higher order frequency moments are calculated as features from the first IMF by periodogram method. Extracted features are classified using Naive Bayes, K-nearest neighbors and Linear Discriminant Analysis methods. From the obtained classification results, it is seen that the Naive Bayes classifier outperforms the K-nearest neighbor and Linear Discriminant Analysis classification methods in pre-seizure and seizure data discrimination with maximum 100% and minimum 77% accuracy.

Obsessive Compulsive Disorder (OCD) is a common neuropsychiatrie disorder in the society. Functional disorders in the frontal side were detected in the clinical evaluation of OCD with brain imaging systems. In this study, the effects of the OCD on the frontal part are investigated with Intertrial Phase Clustering (ITPC) method. According to the analysis results, significant loss of synchronization was found in all EEG bands for 6 electrodes on the frontal region. In addition, that these results are consistent with the results obtained by imaging devices, it was also indicated that decreased synchronization cause functional dysconnectivity. Besides, when evaluated together with the results of studies investigating the relationship between EEG waves and cognitive functions, it also indicates that OCD may cause many cognitive impairments, such as loss of memory and attention.

Obsessive Compulsive Disorder (OCD) is a neuropsychiatrie disorder that usually negatively affects feelings and thoughts of adolescent adversely. Studies of OCD with imaging methods have found intense mental activity in the frontal region. In this study, relative power values are calculated for the channels representing the frontal regions. These values were compared with the data values of the data obtained from healthy volunteers. After statistical analysis, power values of OCD patients were found higher in the teta and alpha frequency regions than in the control group. These values found in the alpha band, which are known to be important for mental activities, show a good deal of previous work done with imaging methods.

Visibility graph analysis of time series became widely used as a time series analysis in the recent years. State transfer network is a network of mapping mono/multivariate time series into a network of local states based on visibility graph, it was used to study the evolutionary behavior of time series and in this study, we applied this principle to the detection of epileptic seizures. Two sets of EEG data were used; first set was obtained from subjects with the healthy brain and the second set obtained from an unhealthy part of the brain during existence of epileptic seizures. Results show a clear discrepancy between the two groups of data with a dominantly appearance of particular nodes in the networks of an epileptic group called hubs or motif, accordingly, the visibility graph network analysis based analysis can be considered as a prediction way of epilepsy seizures.

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.

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. IEEE Turkey Sect

Multivariate time series analysis is of primary importance for the estimation of starting time of epilepsy seizures. Nonlinear synchronization analysis tool called Global Field Synchronization (GFS) was used to estimate the change in synchronization between the two time series. In this study, GFS is applied to the detection of epileptic seizures. Two sets of EEG data were used; first set was obtained from an unhealthy part of the brain prior to the seizure to take place (free seizure interval) and the second set obtained from the opposite healthy hemisphere of the brain. Results show a significant difference on GFS value between selected data sets, where for the unhealthy part shows a lower value of GFS than the healthy part.

In this paper we present a method for the analysis of multichannel EEG by using Generalized Partial Directed Coherence (gPDC) to extract cortical connectivity changes under anesthesia. 15 channel EEG data were recorded from female subjects undergoing general anesthesia for gynecological surgery. Multivariate Autoregressive (MAR) modeling was applied to multichannel, bipolar EEG segments of awake and anesthetized states. gPDCs were calculated using the derived MAR model and averaged through EEG alpha frequency band (8-14 Hz) and through a number of data segments. The gPDC calculation was performed for three different sets of bipolar EEG channel pairs each of which mainly represent a specific scalp area. To derive consistency levels of gPDC values, surrogate analysis is also performed. Using paired t-test for 12 patients, we extracted significant gPDC changes between. awake and anesthetized stages for each set. Analysis revealed that during transition from awake to anesthetized stage, gPDCs of central. to parietal directions were suppressed whereas gPDCs of parietal to central directions were increased. The results indicate that the proposed gPDC analysis method can be used to track the changes in brain connectivity and hence to estimate the depth of anesthesia. (C) 2013 Elsevier Inc. All rights reserved.

Global field synchronization (GFS) quantifies the synchronization level of brain oscillations. The GFS method has been introduced to measure functional synchronization of EEG data in the frequency domain. GFS also detects phase interactions between EEG signals acquired from all of the electrodes. If a considerable amount of local brain neurons has the same phase, these neurons appear to interact with each other. EEG data were received from 17 obsessive-compulsive disorder (OCD) patients and 17 healthy controls (HC). OCD effects on local and large-scale brain circuits were studied. Analysis of the GFS results showed significantly decreased values in the delta and full frequency bands. This research suggests that OCD causes synchronization disconnection in both the frontal and large-scale regions. This may be related to motivational, emotional and cognitive dysfunctions.

In this study, a new method is presented to analyze electroencephalography (EEG) signals by deploying recently proposed adaptive and data driven signal processing method called Empirical Mode Decomposition (EMD). The EMD algorithm represents a signal as a combination of Intrinsic Mode Functions (IMFs) which are extracted from the signal. It is possible to analyze each component of a multi-component signal by using the IMFs. Thus, detrended Fluctuation Analysis (DFA) which is suggested to characterize the auto-correlation properties of non-stationary signals. Frequency and time-frequency domain methods are successfully employed to analyze EEG signals during epileptic seizure. In this study, however, we present a time domain method to analyze and classify EEG signals by investigating the auto-correlation properties of their IMFs extracted by EMD. In the proposed method the IMF features are analyzed by using DFA to determine the epileptic EEG signals.

In this study, a method is presented for the removal of electrooculogram (EOG) noise from electroencephalography (EEG) recordings by using recently proposed data driven approach called Empirical Mode Decomposition (EMD). The EMD represents the signal as a combination of Intrinsic Mode Functions (IMFs). It is an important problem to determine which IMFs belong to signal and noise in multi-component or noisy signals. Detrended Fluctuation Analysis (DFA) is a successful method to characterize non-stationary signals. In our approach, a threshold is determined from the DFA, and used to select the noise IMFs. Performance of the proposed method is demonstrated by means of computer simulations using noisy EEG signals.

Empirical mode decomposition has been recently proposed to analyze non-stationary signals. It decomposes the signal into intrinsic mode functions (IMF) which are derived from the signal itself. However, it is still an unknown issue which IMF involves more information of the signal. In this study, single channel EEG signals from normal and epileptic recordings are analyzed. Hence, mutual information is computed between the autocorrelation function (ACF) of a reference and a given EEG's first IMF. The proposed method is applied to two different datasets to show its classification capability of normal and epileptic EEG signals.

In this paper, we discuss the Bi-frequency Spectrum, which is not used quite often in signal processing applications. We present some cases where the use of this method is necessary by means of several synthetic and EEG signals. We also investigate the stationarity concept related to the new bi-frequency spectrum, and hence present a new perspective of stationarity.

In this study, we present the spatial and temporal evolution of EEG signal spectrum under anaesthesia. Studied features include SEF-90, α-β power ratios, spectral entropy that are known to be used in commercially available depth of anaesthesia monitors. As an additional and comparing feature, we also present Higuchi fractal dimension that is used for analysis of non-linear systems. By means of spatial analysis, we verified the shift of occipitally dominant alpha activity to frontal regions and demonstrated corresponding topographic plots.