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EN
In order to achieve the accurate identifications of various electroencephalograms (EEGs) and electrocardiograms (ECGs), a unified framework of wavelet scattering transform (WST), bidirectional weighted two-directional two-dimensional principal component analysis (BW(2D)2PCA) and grey wolf optimization based kernel extreme learning machine (KELM) was put forward in this study. To extract more discriminating features in the WST domain, the BW(2D)2PCA was proposed based on original two-directional two-dimensional principal component analysis, by considering both the contribution of eigenvalue and the variation of two adjacent eigenvalues. Totally fifteen classification tasks of classifying normal vs interictal vs ictal EEGs, non-seizure vs seizure EEGs and normal vs congestive heart failure (CHF) ECGs were investigated. Applying patient non-specific strategy, the proposed scheme reported ACCs of no less than 99.300 ± 0.121 % for all the thirteen classification cases of Bonn dataset in classifying normal vs interictal vs ictal EEGs, MCC of 90.947 ± 0.128 % in distinguishing non-seizure vs seizure EEGs of CHB-MIT dataset, and MCC of 99.994 ± 0.001 % in identifying normal vs CHF ECGs of BBIH dataset. Experimental results indicate BW(2D)2PCA based framework outperforms (2D)2PCA based scheme, the high-performance results manifest the effectiveness of the proposed framework and our proposal is superior to most existing approaches.
EN
The Brain-computer interface (BCI) is used to enhance the human capabilities. The hybridBCI (hBCI) is a novel concept for subtly hybridizing multiple monitoring schemes to maximize the advantages of each while minimizing the drawbacks of individual methods. Recently, researchers have started focusing on the Electroencephalogram (EEG) and ‘‘Functional Near-Infrared Spectroscopy” (fNIRS) based hBCI. The main reason is due to the development of artificial intelligence (AI) algorithms such as machine learning approaches to better process the brain signals. An original EEG-fNIRS based hBCI system is devised by using the non-linear features mining and ensemble learning (EL) approach. We first diminish the noise and artifacts from the input EEG-fNIRS signals using digital filtering. After that, we use the signals for non-linear features mining. These features are ‘‘Fractal Dimension” (FD), ‘‘Higher Order Spectra” (HOS), ‘‘Recurrence Quantification Analysis” (RQA) features, and Entropy features. Onward, the Genetic Algorithm (GA) is employed for Features Selection (FS). Lastly, we employ a novel Machine Learning (ML) technique using several algorithms namely, the ‘‘Naïve Bayes” (NB), ‘‘Support Vector Machine” (SVM), ‘‘Random Forest” (RF), and ‘‘K-Nearest Neighbor” (KNN). These classifiers are combined as an ensemble for recognizing the intended brain activities. The applicability is tested by using a publicly available multi-subject and multiclass EEG-fNIRS dataset. Our method has reached the highest accuracy, F1-score, and sensitivity of 95.48%, 97.67% and 97.83% respectively.
3
Content available remote Effects of sampling rate on multiscale entropy of electroencephalogram time series
EN
A physiological system encompasses numerous components that function at various time scales. To characterize the scale-dependent feature, the multiscale entropy (MSE) analysis has been proposed to describe the complex processes on multiple time scales. However, MSE analysis uses the relative scale factors to reveal the time-related dynamics, which may cause in-comparability of results from diverse studies with inconsistent sampling rates. In this study, in addition to the conventional MSE with relative scale factors, we also expressed MSE with absolute time scales (MaSE). We compared the effects of sampling rates on MSE and MaSE of simulated and real EEG time series. The results show that the previously found phenomenon (down-sampling can increase sample entropy) is just the projection of the compressing effect of down-sampling on MSE. And we have also shown the compressing effect of down-sampling on MSE does not change MaSE’s profile, despite some minor right-sliding. In addition, by analyzing a public EEG dataset of emotional states, we have demonstrated improved classification rate after choosing appropriate sampling rate. We have finally proposed a working strategy to choose an appropriate sampling rate, and suggested using MaSE to avoid confusion caused by sampling rate inconsistency. This novel study may apply to a broad range of studies that would traditionally utilize sample entropy and MSE to analyze the complexity of an underlying dynamic process.
EN
Automatic seizure detection technology is of great significance to reduce workloads of neurologists for epilepsy diagnosis and treatments. Imbalanced classification is a challenge in seizure detection from long-term continuous EEG recordings, as the durations of the seizure events are much shorter than the non-seizure periods. An imbalanced deep learning model is proposed in this paper to improve the performance of seizure detection. To modify imbalanced EEG data distribution, a generative adversarial network (GAN) that is a strong candidate for data enhancement is built to produce the seizure-period EEG data used for forming a more balanced training set. Next, a pyramidal one-dimensional convolutional neural network (1DCNN) is designed to deal with 1D EEG signals and trained on the augmented training set that consists of both original and generated EEG data. Compared to the conventional 2DCNNs, the deep architecture of the 1DCNN reduces the training parameters so as to greatly increase the training speed. The proposed method is evaluated on three publicly available EEG databases. After data augmentation by the GAN, the designed 1DCNN shows much better classification for seizure detection, achieving competitive results over the three EEG databases, which demonstrates the generalizability of this method across different databases. Comparison with other published methods indicates its enhanced detection performance for imbalanced EEG data.
EN
The excessive drinking of alcohol can disrupt the neural system. This can be observed by properly analysing the Electroencephalogram (EEG) signals. However, the EEG is a signal of complex nature. Therefore, an accurate categorization between alcoholic (A) and nonalcoholic (NA) subjects, while using a short time EEG recording, is a challenging task. In this paper a novel hybridization of the oscillatory modes decomposition, features mining based on the Second Order Difference Plots (SODPs) of oscillatory modes, and machine learning algorithms is devised for an effective identification of alcoholism. The Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are used to respectively decompose the considered EEG signals in Intrinsic Mode Functions (IMFs) and Modes. Onward, the SODPs, derived from first six IMFs and Modes, are considered. Features of SODPs are mined. To reduce the dimension of features set and computational complexity of the classification model, the pertinent features selection is made on the basis of Wilcoxon statistical test. Three features with p-values (p) of < 0.05 are selected from each intended SODP and these are the Central Tendency Measure (CTM), area and mean. These features are used for the discrimination between A and NA classes. In order to determine a suitable EEG signal segment length for the intended application, experiments are performed by considering features extracted from three different length time windows. The classification is carried out by using the Least Square Support Vector Machine (LS-SVM), Multilayer perceptron neural network (MLPNN), K-Nearest Neighbour (KNN) and Random Forest (RF) algorithms. The applicability is tested by using the UCI-KDD EEG dataset. The results are noteworthy for MLPNN with 99.89% and 99.45% accuracies for EMD and VMD respectively for 8-second window.
EN
Epileptic seizures result from disturbances in the electrical activity of the brain, classified as focal, generalized, or unknown. Failure to correctly classify epileptic seizures may result in inappropriate treatment and continuation of seizures. Therefore, automatic detection of generalized, focal, and other epileptic seizures from EEG signals is important. In this research article, Focal-Generalized classification method is proposed that compares traditional classification algorithms and deep learning methods. Two different classifications: four-class (Case (I) Complex Partial Seizure (CPSZ) (C4-T4 Onset)-CPSZ (FP2-F8 Onset)-CPSZ (T5-O1 Onset)- Absence Seizure (ABSZ)) and two-class (Case (II) CPSZ-ABSZ) problems are considered. This study includes preprocessing of scalp Electroencephalogram (EEG) data, feature extraction with discrete wavelet method, feature selection using Correlation-based Feature Selection (CFS) method, and classification of data with classifier algorithms (K-Nearest Neighbors (Knn), Support Vector Machine (SVM), Random Forest (RF) and Long Short-Term Memory (LSTM). The proposed method was applied on 23 subjects in the Temple University Hospital (TUH) scalp EEG data set, and a classification success rate of 95,92% for case (I) and 98,08% for case (II) was successfully achieved with deep learning architecture LSTM.
EN
Depression is one of the significant contributors to the global burden disease, affecting nearly 264 million people worldwide along with the increasing rate of suicidal deaths. Electroencephalogram (EEG), a non-invasive functional neuroimaging tool has been widely used to study the significant biomarkers for the diagnosis of the disorder. Computational Psychiatry is a novel avenue of research that has shown a tremendous success in the automated diagnosis of depression. The present comprehensive review concentrate on two approaches widely adopted for an EEG based automated diagnosis of depression: Deep Learning (DL) approach and the traditional approach based upon Machine Learning (ML). In this review, we focus on performing the comparative analysis of a variety of signal processing and classification methods adopted in the existing literature for these approaches. We have discussed a variety of EEG based objective biomarkers and the data acquisition systems adopted for the diagnosis of depression. Few EEG studies focusing on multimodal fusion of data have also been explained. Additionally, the research based upon the analysis and prediction of treatment outcome response for depression using EEG signals and machine learning techniques has been briefly discussed to aware the researchers about this emerging field. Finally, the future opportunities and a valuable discussion on major issues related to this field have been summarized that will help the researchers in developing more reliable and computationally intelligent systems in the field of psychiatry.
EN
This work presents a new epileptic seizures epoch classification scheme. Variational mode decomposition (VMD), has been explored for non-recursively decomposing the electroencephalogram (EEG) signals into fourteen band limited intrinsic mode functions (IMFs). Data augmentation (DA), has been used for handling unbalanced classification problem. Normalized energy, fractal dimension, number of peaks, and prominence parameters were computed from the band-limited IMFs for the discrimination of seizure and non-seizure epochs. Bayesian regularized shallow neural network (BR-SNNs) and six other well-known classifiers were tested. Sensitivity, specificity, and accuracy have been used as performance metrics. This study includes two different epoch lengths of 1-second and 2-seconds. A total of 32 test cases for both, class balanced and unbalanced classification problems have been taken for the performance evaluation. The best performance obtained is 100% for all the three metrics from the test cases of database-2 and 3. For database-1, average sensitivity, specificity, and accuracy of 99.71, 99.75, and 99.73% have been achieved, respectively for the 1-second epoch. The presented work shows better performance results compared to many previously reported works.
EN
Major Depressive Disorder (MDD) is one of the leading causes of disability worldwide. Prediction of response to Selective Serotonin Reuptake Inhibitors (SSRIs) antidepressants in patients with MDD is necessary for preventing side effects of mistreatment. In this study, a deep Transfer Learning (TL) strategy based on powerful pre-trained convolutional neural networks (CNNs) in the big data datasets is developed for classification of Responders and Non-Responders (R/NR) to SSRI antidepressants, using 19-channel Electroencephalography (EEG) signal acquired from 30 MDD patients in the resting state. Multiple time-frequency images are obtained from each EEG channel using Continuous Wavelet Transform (CWT) for feeding into pre-trained CNN models that are VGG16, Xception, DenseNet121, MobileNetV2 and InceptionResNetV2. Our plan is to adapt and fine-tune the weights of networks to the target task with the small-sized dataset. Finally, to improve the recognition performance, an ensemble method based on majority voting of outputs of five mentioned deep TL architectures has been developed. Results indicate that the best performance among basic models achieved by DenseNet121 with accuracy, sensitivity and specificity of 95.74%, 95.56% and 95.64%, respectively. An Ensemble of these basic models created to surpass the accuracy obtained by each individual basic model. Our experiments show that ensemble model can gain accuracy, sensitivity and specificity of 96.55%, 96.01% and 96.95%, respectively. Therefore, proposed ensemble of TL strategy of pre-trained CNN models based on WT images obtained from EEG signal can be used for antidepressants treatment outcome prediction with a high accuracy.
EN
Alcoholism can be analyzed by Electroencephalogram (EEG) data. Finding an optimal subset of EEG channels for alcoholism detection is a challenging task. The paper reports a new methodology for the detection of optimal channels for alcoholism analysis using EEG data. The proposed technique employs the Empirical Mode Decomposition (EMD) technique to extract the amplitude and frequency modulated bandwidth features from the Intrinsic Mode Function (IMF) and ensemble subspace K-NN as a classifier to classify alcoholics and normal. The optimum channels are selected, using a harmony search algorithm. The fitness value of discrete binary harmony search (DBHS) optimization algorithms is calculated using accuracy and sensitivity achieved by the ensemble subspace K-Nearest Neighbor classifier. Experimental outcomes indicate that the optimal channel selected by the harmony search algorithm has biological inference related to the alcoholic subject. The proposed approach reports a classification accuracy of 93.87%, with only 12 detected EEG channels.
11
Content available remote FPGA based real-time epileptic seizure prediction system
EN
The development of systems that can predict epileptic seizures in real-time offers great hope for epilepsy patients. These systems aim to prevent accidents that patients may experience caused by the loss of consciousness during seizures. Therefore, patients must use real-time epileptic seizure prediction systems that do not interfere with their daily activities. In this study, using the unipolar EEG data from a surface electrode, a patient-specific estimation system is implemented in real-time on a system on chip (SoC) that contains an embedded processor and programmable logic blocks. The European epilepsy database EPILEPSIAE is used in the scope of this work. In the proposed system, pre-processing is applied to the EEG data. Then, the features of the data in the frequency domain are extracted. The classifier model is trained with the RusBoosted Tree cluster classifier, which is a machine learning algorithm. Testing is carried out using the proposed classification model. Threshold values are determined, and then false alarms and erroneous classifications are prevented by post-processing. At the end of the tests, prediction success, sensitivity (SEN), Specificity (SPE), False Prediction Rate (FPR), and prediction times are obtained as 77.30%, 95.94%, 0.041 h_1, and 33.23 min, respectively. The proposed system outperforms other studies in the liter-ature in the number of electrodes, real-time operation, hardware/software architecture, and FPR performance. A wearable seizure prediction system seems to be commercialized according to the results achieved in this study.
12
Content available remote Epileptic seizure prediction using scalp electroencephalogram signals
EN
Epilepsy is a brain disorder in which patients undergo frequent seizures. Around 30% of patients affected with epilepsy cannot be treated with medicines/surgical procedures. Abnormal activity, known as the preictal state starts few minutes before the seizure actually occurs. Therefore, it may be possible to deliver medication prior to the occurrence of a seizure if initiation of the preictal state can predicted before the seizure onset. We propose an epileptic seizure prediction method that predicts the preictal state before the seizure onset using electroencephalogram (EEG) monitoring of brain activity. It involves three steps including preprocessing of EEG signals, feature extraction classification of preictal and interictal states. In our proposed method, we have used (i) Empirical model decomposition to remove noise from the EEG signals and Generative Adversarial Networks to generate preictal samples to deal with the class imbalance problem; (ii) Automated features have been extracted with three layer Convolutional Neural Networks and (iii) Classification between preictal and interictal states is done with Long Short Term Memory units. In this study, we have used CHBMIT dataset of scalp EEG signals and have validated our proposed method on 22 subjects of dataset. Our proposed seizure prediction method is able to achieve 93% sensitivity and 92.5% specificity with average time of 32 min to predict the seizure's onset. Results obtained from our method have been compared with recent state-of-the-art epileptic seizure prediction methods. Our proposed method performs better in terms of sensitivity, specificity and average anticipation time.
EN
In the field of human-computer interaction, the detection, extraction and classification of the electroencephalogram (EEG) spectral and spatial features are crucial towards developing a practical and robust non-invasive EEG-based brain-computer interface. Recently, due to the popularity of end-to-end deep learning, the applicability of algorithms such as convolutional neural networks (CNN) has been explored to achieve the mentioned tasks. This paper presents an improved and compact CNN algorithm for motor imagery decoding based on the adaptation of SincNet, which was initially developed for speaker recognition task from the raw audio input. Such adaptation allows for a compact end-to-end neural network with state-of-the-art (SOTA) performances and enables network interpretability for neurophysiological validation in cortical rhythms and spatial analysis. In order to validate the performance of proposed algorithms, two datasets were used; the first is the publicly available BCI Competition IV dataset 2a, which was often used as a benchmark in validating motor imagery classification algorithms, and the second is a dataset consists of primary data initially collected to study the difference between motor imagery and mental-task associated motor imagery BCI and was used to test the plausibility of the proposed algorithm in highlighting the differences in terms of cortical rhythms. Competitive decoding performance was achieved in both datasets in comparisons with SOTA CNN models, albeit with the lowest number of trainable parameters. In addition, it was shown that the proposed architecture performs a cleaner band-pass, highlighting the necessary frequency bands that were crucial and neurophysiologically plausible in solving the classification tasks.
14
Content available remote Analysis of epileptic EEG signals by using dynamic mode decomposition and spectrum
EN
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.
15
Content available remote An improved MAMA-EMD for the automatic removal of EOG artifacts
EN
The separation of electrooculogram (EOG) and electroencephalogram (EEG) is a potential problem in brain-computer interface (BCI). Especially, it is necessary to accurately remove EOG, as a disturbance, from the measured EEG in brain disease diagnosis, EEG-based rehabilitation systems, etc. Due to the interaction between the eye and periocular musculature, a multipoint spike is often produced in EEG for each ocular activity. Masking-aided minimum arclength empirical mode decomposition (MAMA-EMD) was developed to robustly decompose time series with impulse-like noise. However, the decomposition performance of MAMA-EMD was limited in the case of one impulse with multiple contiguous spike points. In this paper, MAMA-EMD was improved (called IMAMA-EMD) by supplementing the minimum arclength criterion, and it was combined with kernel independent component analysis (KICA), yielding an automatic EOG artifact removal method, denoted as KIIMME. The multi-channel contaminated EEG signals were separated into several independent components (ICs) by KICA. Then, IMAMA-EMD was applied to the EOG-related ICs decomposition to generate a set of inherent mode functions (IMFs), the low frequency ones, which have higher correlation with EOG components, were removed, and the others were employed to construct ‘clean’ EEG. The proposed KIIMME was evaluated and compared with other methods on semisimulated and real EEG data. Experimental results demonstrated that IMAMA-EMD effectively eliminated the influence of multipoint spike on sifting process, and KIIMME improved the removal accuracy of EOG artifacts from EEG while retaining more useful neural data. This improvement is of great significance to research on brain science as well as BCI.
16
Content available remote The Status of Textile-Based Dry EEG Electrodes
EN
Electroencephalogram (EEG) is the biopotential recording of electrical signals generated by brain activity. It is useful for monitoring sleep quality and alertness, clinical applications, diagnosis, and treatment of patients with epilepsy, disease of Parkinson and other neurological disorders, as well as continuous monitoring of tiredness/alertness in the field. We provide a review of textile-based EEG. Most of the developed textile-based EEGs remain on shelves only as published research results due to a limitation of flexibility, stickability, and washability, although the respective authors of the works reported that signals were obtained comparable to standard EEG. In addition, nearly all published works were not quantitatively compared and contrasted with conventional wet electrodes to prove feasibility for the actual application. This scenario would probably continue to give a publication credit, but does not add to the growth of the specific field, unless otherwise new integration approaches and new conductive polymer composites are evolved to make the application of textile-based EEG happen for bio-potential monitoring.
EN
The Electroencephalogram (EEG) recordings from the frontal lobe of the human brain help in analyzing several important brain functions like motor functions, problem-solving skills, etc. or brain disorders. These recordings are often contaminated by high amplitude and long duration ocular artifacts (OAs) like eye blinks, flutters and lateral eye movements (LEMs), hence corrupting a considerable segment of EEG. In this study, an enhanced version of signal decomposition scheme i.e. Variational Mode Decomposition (VMD) based algorithm is used for suppression of OAs. The signal decomposition is preceded by identification of ocular artifact corrupted segment using Multiscale modified sample entropy (mMSE). The band limited intrinsic mode functions (BLIMFs) are obtained using predefined K (number of required BLIMFs) and α (balancing parameter). These parameters help to detrend the EEG segment in yielding the low frequency and high amplitude BLIMFs related to OA efficiently. Upon identifying OA components from the BLIMFs and estimating OA, it is regressed with the contaminated EEG to obtain the clean EEG. The proposed VMD based algorithm provides an improved performance in comparison with the existing single channel algorithms based on Empirical mode decomposition (EMD) and Ensembled EMD (EEMD) and multi-channel algorithms like Independent component analysis (ICA) and wavelet enhanced ICA for artifact suppression and is also able to overcome their limitations. The significance of the algorithm are: (1) no additional reference EOG channel requirement, (2) OA artifact based thresholds for identification and estimation from the mode functions obtained using VMD, and (3) also address the flutter artifacts.
EN
This study investigates the properties of the brain electrical activity from different recording regions and physiological states for seizure detection. Neurophysiologists will find the work useful in the timely and accurate detection of epileptic seizures of their patients. We explored the best way to detect meaningful patterns from an epileptic Electroencephalogram (EEG). Signals used in this work are 23.6 s segments of 100 single channel surface EEG recordings collected with the sampling rate of 173.61 Hz. The recorded signals are from five healthy volunteers with eyes closed and eyes open, and intracranial EEG recordings from five epilepsy patients during the seizure-free interval as well as epileptic seizures. Feature engineering was done using; i) feature extraction of each EEG wave in time, frequency and time-frequency domains via Butterworth filter, Fourier Transform and Wavelet Transform respectively and, ii) feature selection with T-test, and Sequential Forward Floating Selection (SFFS). SVM and KNN learning algorithms were applied to classify preprocessed EEG signal. Performance comparison was based on Accuracy, Sensitivity and Specificity. Our experiments showed that SVM has a slight edge over KNN.
EN
Personal identification is particularly important in information security. There are numerous advantages of using electroencephalogram (EEG) signals for personal identification, such as uniqueness and anti-deceptiveness. Currently, many researchers focus on single-dataset personal identification, instead of the cross-dataset. In this paper, we propose a method for cross-dataset personal identification based on a brain network of EEG signals. First, brain functional networks are constructed from the phase synchronization values between EEG channels. Then, some attributes of the brain networks including the degree of a node, the clustering coefficient and global efficiency are computed to form a new feature vector. Lastly, we utilize linear discriminant analysis (LDA) to classify the extracted features for personal identification. The performance of the method is quantitatively evaluated on four datasets involving different cognitive tasks: (i) a four-class motor imagery task dataset in BCI Competition IV (2008), (ii) a two-class motor imagery dataset in the BNCI Horizon 2020 project, (iii) a neuromarketing dataset recorded by our laboratory, (iv) a fatigue driving dataset recorded by our laboratory. Empirical results of this paper show that the average identification accuracy of each data set was higher than 0.95 and the best one achieved was 0.99, indicating a promising application in personal identification.
EN
Purpose: Visual inspection of electroencephalogram (EEG) records by neurologist is the main diagnostic method of epilepsy but it is particularly time-consuming and expensive. Hence, it is of great significance to develop automatic seizure detection technique. Methods: In this work, a seizure detection approach, synthesizing generalized Stockwell transform (GST), singular value decomposition (SVD) and random forest, was proposed. Utilizing GST, the raw EEG was transformed into a time–frequency matrix, then the global and local singular values were extracted by SVD from the holistic and partitioned matrices of GST, respectively. Subsequently, four local parameters were calculated from each vector of local singular values. Finally, the global singular value vectors and local parameters were respectively fed into two random forest classifiers for classification, and the final category of a testing EEG was voted based on sub-labels obtained from the trained classifiers. Results: Four most common but challenging classification tasks of Bonn EEG database were investigated. The highest accuracies of 99.12%, 99.63%, 99.03% and 98.62% were achieved using our presented technique, respectively. Conclusions: Our proposed technique is comparable or superior to other up-to-date methods. The presented method is promising and able to handle with kinds of epileptic seizure detection tasks with satisfactory accuracy.
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