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1

Identifying epileptic EEGs and congestive heart failure ECGs under unified framework of wavelet scattering transform, bidirectional weighted (2D)2PCA and KELM

Zhang Tao, Chen Wanzhong, Chen Xiaojuan

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

279--297

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.

2

Hybrid EEG-fNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learning

Maher Asmaa, Mian Qaisar Saeed, Salankar N., Jiang Feng, Tadeusiewicz Ryszard, Pławiak Paweł, Abd El-Latif Ahmed A., Hammad Mohamed

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 2

463--475

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

Effects of sampling rate on multiscale entropy of electroencephalogram time series

Zheng Jinlin, Li Yan, Zhai Yawen, Zhang Nan, Yu Haoyang, Tang Chi, Yan Zheng, Luo Erping, Xie Kangning

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

233--245

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.

4

Effects of Bikram Yoga Clothes on EEG Beta Spectrum

Lee Hyojeong, Lee Yejin

Fibres & Textiles in Eastern Europe

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2022

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Vol. 30, no. 6

45--54

EN

This study analyzes how the beta index, which is closely related to alertness, caution, concentration, anxiety, and tension in brain activity, varies before and after practicing yoga. Electroencephalogram (EEG) and subjective evaluations were conducted on healthy female yoga trainers with over three years of experience; participants wore yoga clothes with differing characteristics in a hot environment. Repeated ANOVA measurements were conducted on the data by deriving the difference between the corresponding sample t-test pre- and post-yoga . After yoga, concentration increased, while alertness, anxiety, and excitement decreased depending on the yoga clothes. The clothing combination that offered higher pressure and greater absorption, and enhanced concentration while lowering excitation and anxiety increased beta waves the most. The design characteristics of yoga clothes influence beta power for concentration and arousal after yoga practice. Through EEG measurements, it was possible to explore the mental states resulting from wearing clothes suitable for yoga.

5

EEG based alcoholism detection by oscillatory modes decomposition second order difference plots and machine learning

Salankar Nilima, Qaisar Saeed Mian, Pławiak Paweł, Tadeusiewicz Ryszard, Hammad Mohamed

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

173--186

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.

6

Channel based epilepsy seizure type detection from electroencephalography (EEG) signals with machine learning techniques

Tuncer Erdem, Bolat Emine Doğru

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 2

575--595

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.

7

Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis

Khosla Ashima, Khandnor Padmavati, Chand Trilok

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

108--142

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.

8

The Influence of a Low-Frequency Musical Fragment on the Neural Oscillations

Drozdenko Kateryna, Naida Sergey, Drozdenko Oleksandr, Damarad Anastasiia, Pareniuk Dmytro, Vakulenko Liudmyla, Adaricheva Zhanna

Archives of Acoustics

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2022

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Vol. 47, No. 2

169--179

EN

Study of musical-acoustic influences, which are used to improve the functional state of a person, as well as her/his neurophysiological or psychological rehabilitation, is very relevant nowadays. It is related with a large number of conflict situations, significant psychological and informational overloads of modern human, permanent stress due to the pandemic, economic crisis, natural and man-made disasters. This work examines the effect of listening to low-frequency music on the percentage of alpha, beta, delta, and theta waves in the total spectral power of the electroencephalogram in the frequency band 0.5–30 Hz. To obtain rhythms of the brain the spectral analysis of filtered native electroencephalogram was used. For statistical analysis of neural oscillations the Student’s t-test and the sign test were implemented with usage of the Lilliefors normality criterion and the Shapiro-Wilk test. Statistically significant differences were identified in alpha, theta and delta oscillations. For the beta rhythm presented music did not play any significant role. An increase in the activity of the alpha rhythm in the temporal (for 2.20 percentage point), central (for 1.51 percentage point), parietal (for 2.70 percentage point), occipital (for 2.22 percentage point) leads of the right hemisphere and the parietal (for 1.74 percentage point) and occipital (for 2.46 percentage point) leads of the left hemisphere and also of the theta rhythm in the temporal leads of the left hemisphere (for 1.13 percentage point) were observed. The downfall of delta rhythm in the frontal lead of the left hemisphere (for 1.51 percentage point) and occipital in both hemispheres (for 1.64 and 1.33 percentage points respectively in the left and right hemispheres) was detected. These may indicate that listening to low-frequency compositions helps to restore the brain in physiological conditions at different functional overload levels, decrease the level of emotional tone, and promote relaxation.

9

Modified Block Sparse Bayesian Learning-Based Compressive Sensing Scheme For EEG Signals

Upadhyaya Vivek, Salim Mohammad

International Journal of Electronics and Telecommunications

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2021

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Vol. 67, No. 3

331--336

EN

Advancement in medical technology creates some issues related to data transmission as well as storage. In real-time processing, it is too tedious to limit the flow of data as it may reduce the meaningful information too. So, an efficient technique is required to compress the data. This problem arises in Magnetic Resonance Imaging (MRI), Electrocardiogram (ECG), Electroencephalogram (EEG), and other medical signal processing domains. In this paper, we demonstrate Block Sparse Bayesian Learning (BSBL) based compressive sensing technique on an Electroencephalogram (EEG) signal. The efficiency of the algorithm is described using the Mean Square Error (MSE) and Structural Similarity Index Measure (SSIM) value. Apart from this analysis we also use different combinations of sensing matrices too, to demonstrate the effect of sensing matrices on MSE and SSIM value. And here we got that the exponential and chi-square random matrices as a sensing matrix are showing a significant change in the value of MSE and SSIM. So, in real-time body sensor networks, this scheme will contribute a significant reduction in power requirement due to its data compression ability as well as it will reduce the cost and the size of the device used for real-time monitoring.

10

Variational mode decomposition-based seizure classification using Bayesian regularized shallow neural network

Yadav Vipin Prakash, Sharma Kamlesh Kumar

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

402--418

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.

11

Prediction of drug response in major depressive disorder using ensemble of transfer learning with convolutional neural network based on EEG

Sadat Shahabi Mohsen, Shalbaf Ahmad, Maghsoudi Arash

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

946--959

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.

12

Optimal EEG channels selection for alcoholism screening using EMD domain statistical features and harmony search algorithm

Bavkar Sandeep, Iyer Brijesh, Deosarkar Shankar

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

83--96

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.

13

FPGA based real-time epileptic seizure prediction system

Cosgun Ercan, Çelebi Anil

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

278--292

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.

14

Compact convolutional neural network (CNN) based on SincNet for end-to-end motor imagery decoding and analysis

Izzuddin Tarmizi Ahmad, Safri Norlaili Mat, Othman Mohd Afzan

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 4

1629--1645

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.

15

Automatic detection for epileptic seizure using graph-regularized nonnegative matrix factorization and Bayesian linear discriminate analysis

Mu Jianwei, Dai Lingyun, Liu Jin-Xing, Shang Junliang, Xu Fangzhou, Liu Xiang, Yuan Shasha

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 4

1258--1271

EN

Epilepsy is a neurological disorder characterized by excessive neuronal discharge which results in many problems in terms of behavior, state of mind, consciousness, and can threaten the lives of patients. An automatic epileptic seizure detection method with graph-regularized non-negative matrix factorization (GNMF) and Bayesian linear discriminate analysis (BLDA) is presented in this paper. First, discrete wavelet decomposition is applied to analyze raw electroencephalogram (EEG) signals, and the normalization based on differential operator is used to guarantee the nonnegative constraint and reinforce the distinction between seizure and non-seizure signals. Then, GNMF is employed to dimensionality reduction and feature extraction for EEG data, which could capture a parts-based representation of samples and obtain more discriminative features. The EEG features are calculated and entered into the BLDA classifier for categorized results. The public Freiburg EEG database is used to evaluate the performance of the proposed seizure detection method. The results showed event-based sensitivity of 95.24%, epoch-based sensitivity of 93.20%, and a false-alarm rate of 0.5/h. These results demonstrate the potential clinical value of this method for automatic seizure detection.

16

Analysis of epileptic EEG signals by using dynamic mode decomposition and spectrum

Karabiber Cura Ozlem, Akan Aydin

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

28--44

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.

17

An improved MAMA-EMD for the automatic removal of EOG artifacts

Li Mingai, Zhang Yuanyuan

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

1182--1196

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.

18

Computational model of decreased suppression of mu rhythms in patients with Autism Spectrum Disorders during movement observation - preliminary findings

Zapała Dariusz, Mikołajewski Dariusz

Bio-Algorithms and Med-Systems

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2021

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Vol. 17, no. 2

95--102

EN

Objectives: Autism Spectrum Disorders (ASD) represent developmental conditions with deficits in the cognitive, motor, communication and social domains. It is thought that imitative behaviour may be impaired in children with ASD. The Mirror Neural System (MNS) concept plays an important role in theories explaining the link between action perception, imitation and social decision-making in ASD. Methods: In this study, Emergent 7.0.1 software was used to build a computational model of the phenomenon of MNS influence on motion imitation. Seven point populations of Hodgkin-Huxley artificial neurons were used to create a simplified model. Results: The model shows pathologically altered processing in the neural network, which may reflect processes observed in ASD due to reduced stimulus attenuation. The model is considered preliminary-further research should test for a minimally significant difference between the states: normal processing and pathological processing. Conclusions: The study shows that even a simple computational model can provide insight into the mechanisms underlying the phenomena observed in experimental studies, including in children with ASD.

19

The Status of Textile-Based Dry EEG Electrodes

Tseghai Granch Berhe, Malengier Benny, Fante Kinde Anlay, Van Langenhove Lieva

Autex Research Journal

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2021

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Vol. 21, no. 1

63--70

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.

20

An improved algorithm for efficient ocular artifact suppression from frontal EEG electrodes using VMD

Dora Chinmayee, Biswal Pradyut Kumar

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

148--161

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.