Classification of EEG signals for brain-computer interface applications: Performance comparison

Detection of congestive heart failure from short-term heart rate variability segments using hybrid feature selection approach

Analysis of brain activities and remote control are among the current issues that are being studied. Analysis of signals arising during brain functions is electroencephalography (EEG). EEG signals have intellectual, visual stimulation, and motion resultant forms. Especially, EEG signals generated by visual stimulus are within the scope of this study. In this study, research was carried out on the classification of EEG signals formed in a person looking at visual figures. For these studies, first of all, EEG signals from the brain were recorded with images and filtered to remove noise. Then, the features were extracted from the signals. In this study, Moment 5 feature was also used in addition to the features used in many studies such as mean, median, standard deviation and entropy. Then, classification was made using Support Vector Machine (SVM), k Nearest Neighbor (KNN), and Decision Tree (DT) algorithms. Classification was made for 4 different visual shapes used, since these shapes are square, circle, triangle, and star, and the same categorical names were used in the classification stage. As a result of the classification of EEG signals; SVM and KNN algorithms have determined which shape is viewed with 99.99% accuracy. These results show that different signals are produced in the brain according to the structure of the shape viewed. This situation shows that it can be used as a method to give patients the opportunity to express their requests just by looking or thinking.

The evolution of machine learning (ML) algorithms provides researchers and engineers with state-of-the-art tools to dynamically model complex relationships. The design and operation of municipal solid waste (MSW) management systems require accurate estimation of generation rates. In this study, we applied rapid, non-linear and non-parametric data driven ML algorithms independently, multi-layer perceptron artificial neural network (MLP-ANN) and support vector regression (SVR) models to predict annual MSW generation rates in Bahrain. Models were trained and tested with MSW generation data for period of 1997-2019. The population, gross domestic product, annual tourist numbers, annual electricity consumption and total annual CO2 emissions were selected as explanatory variables and incorporated into developed models. The zero score normalization (ZSN) and minimum maximum normalization (MMN) methods were utilized to improve the quality of data and subsequently enhances the performance of ML algorithms. Statistical metrics were employed to discriminate performance of MLP-ANN and SVR models. The linear, polynomial, radial basis function (RBF) and sigmoid kernel functions were investigated to find the optimal SVR model. Results showed that RBF-SVR model with R2 value of 0.97% and 4.82% and absolute forecasting error (AFE) for the period of 2008 and 2019 exhibits superior prediction and robustness in comparison to MLP-ANN. The efficacy of MLP-ANN model was also reasonably successful with R2 value of 0.94. It was shown that MMN pre-processing generated optimal MLP-ANN model while ZSN pre-processing produced optimal RBF-SVR model. This work also highlights the importance of application of ML modelling approaches to plan and implement their roadmap for waste management systems by policymakers.

Classification of EEG signals using neural network and logistic regression

Brain-Computer Interface (BCI) or Human-Machine Interface now becoming vital biomedical engineering and technology field which applying EEG technologies to provide assistive device technology (AT) to humans. Hence, this paper presents the results of analyzing EEG signals from various human cognitive states to extract the suitable EEG features that can be employed to control BCI devices which can be used by disabled or paralyzed people. The EEG features in term of power spectral density, spectral centroids, standard deviation and entropy are selected and investigated from two different mental exercises; (i) quick solving math and (ii) relax (do nothing). The selected features then are classified using Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K-Nearest Neighbors (k-NN) classifier. Among all these features, the best accuracy have been achieved by the power spectral density. The accuracies of this feature are 95%, 100%, 100% with LDA, SVM and K-NN respectively. Finally, the translation algorithm will be constructed using selected and classified EEG features to control the BCI devices.

Data driven simulations for accurately predicting thermodynamic properties of H2 during geological storage

SUMMARY The paper aims demonstration of the ML applicability to the problem of rock porosity structure studying by the combination of sonic and density well logs. The experimentally estimated efficiency of popular ML methods for the problem is discussed. In the test we used artificial samples of randomly generated structure with the well log parameters computed by Prodaivoda-Maslov's method of the direct problem solving. Among the many known algorithms of ML, we selected for the study several ones which are popular and supported by standard Python libraries K-Nearest Neighbors (KNN), Logistic Regression (LRM), a feed forward artificial neural network Multilayer Perceptron (in both the classification form MLPC and the regression form MLPR), Support Vector Machine (SVM), Decision Tree (ID3) and Random Forest (Forest). We subdivided the problem by independent sub-problems of estimation the concentration of different aspect ratio inclusions a1 cracks (10-3: disks), a2 micro cracks (10-2: disks), a3 pores (100: spheres), a4 caverns (102: streaks). To reconstruct simultaneously 4 unknown parameters we applied multi-task learning. Here represents results only for a1. The classification algorithms performed generally worse in respect of MAE. Yet the error of about 5% was expected here because the classes were defined by 10% concentration intervals (0–10%, 10–20%, and so on). More interesting is their inability to identify right class. It is expressed by the accuracy score. The best classification algorithm MLPC leaded in both MAE and classification accuracy competitions. But its classification accuracy score is only 72. 4%. The tests have demonstrated the ability of machine learning algorithms to estimate concentration of a known subtype inclusions on the base of sonic logs and density. The best regression algorithm, Random Forest, with its Mean Absolute Error MAE = 1. 7% in concentration provides excellent quality. Two other good reg ression algorithms demonstrate acceptable MAE < 5%. It would be interesting to apply the ML methods to real core data. We invite for collaboration those who have access to core collections and the ability to execute more detailed analysis of the core porosity than usually.

In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices.

For developing brain computer interface (BCI) applications, electroencephalography (EEG) is the most widely used measurement method due to its noninvasiveness, high temporal resolution, and portability. EEG signal contains sufficient neural information about each human task, which makes the extracting, and decoding of each task-related information is still challenging, especially to improve the existing BCI performances. In this paper, we present a comparison analysis to find the most relevant features and the most suitable classification method for decoding motor imagery for EEG-based BCI. Therefore, some signal processing and machine learning techniques have applied for features extraction and classification phases. For the decomposition of EEG signal, we used three type of features [EEG signal mean, root mean square (RMS) and Relative of band power (RBP)]. In addition, we investigated an analytical comparison between three methods of classification [Support Vector Machine (SVM), Linear Discriminant Analysis and K-Nearest Neighbors]. The methods were validated using a publicly available dataset (BCI Competition IV-III-a) to discriminate between two mental states (right and left hand movements) using 10-fold cross-validation. SVM method gave better classification accuracy of 76.4% using relative band powers as potential EEG features.

Epilepsy is a chronic brain disorder recognized by amalgamation of sudden transient electrical disturbance. The electroencephalogram (EEG) is an relevant tool for analyzing brain related disorders. Currently, manual reading of EEG is widely adopted by neurologists to diagnose epilepsy and related disorders. The process is erroneous as it involves fatigues due to continuous monitoring of nonstationary signals. The automated seizure detection may assist neurologist to automate the process and diagnose seizure signals with high accuracy. The nonlinearity of the EEG signals is evaluated by decomposing the signals for various types of filters according to the oscillatory behaviour of the signals. The computation of spectral features from the subbands obtained from Discrete Wavelet Transform (DWT) are used as a feature for classifying the different classes of test signals. In this work, two different classification problems have been considered on bivariate signals. First problem is normal (N) and ictal (I) and second is normal (N), inter-ictal (IC) and ictal (I). Features obtained from the EEG signals of these classes are fed to the input of three different classifiers namely, Multilayer Perceptron Artificial Neural Networks (MLPNN) and Least Squares Support Vector Machine (LSSVM). For N-I and N-IC-I classes, we achieved 97.8% classification accuracy using MLPNN on focal and non-focal data from the University of Bern, Barcelona.

Event Abstract Back to Event Analysis and classification of EEG signals for brain computer interfaces Ugur Halici1*, E. Agi1, C. Özgen1 and I. Ulusoy1 1 Computer Vision and Intelligent Systems Research Laboratory, Middle East Technical University, Turkey Brain Computer Interfaces (BCIs) are systems developed in order to control devices by using only brain signals. Depending on the application, the device to be controlled might be a neuroprothesis, a wheel-chair or a computer. In BCI systems, different mental activities to be performed by the users are associated with different actions on the device to be controlled. The neurophysiologic brain signals produced as results of these mental activities are collected by a data acquisition device and converted to digital form. Electroencephalogram (EEG) is the most common technique employed for data acquisition in BCI systems today, due to its time-space resolution, its price and its easiness in use when compared to the other acquisition techniques. The digital brain signals collected are processed for feature extraction and then classified in order to decide on the associated action. A control signal produced according to the decision is used to change the state of the device to be controlled. Providing a feedback signal informing the user about the new state of the controlled device completes the BCI cycle. The early BCI systems were designed to work on pre-defined neurophysiologic features, where a feedback produced by a fixed set-up was provided to the system users. In these early BCI systems, the users should learn by themselves how to produce neurophysiologic signals in order to control the system. In several studies, it is reported that weeks, even months may be needed in order the user to learn how to produce these signals by adaptation of their brain. Recently, BCI systems in which machine learning algorithms employed for adapting the BCI system by using user specific signals are started to emerge. In several studies, it is shown that the adaptation of the BCI systems in this way reduces drastically the time required the user to able to produce appropriate signals and adaptation time in terms of days, even minutes are reported [1-4] The aim of this study is to analyze EEG signals collected for BCI purposes by using techniques Principal Component Analysis, Fisher Linear Discriminant Analysis, and Independent Component Analysis in order to reduce the dimension of the feature vectors obtained by sampling EEG signals in an representative manner and then classify the low dimension feature vectors obtained in this way by using Neural Networks and Support Vector Machines, which are able to learn through samples. The experimental studies are still going on for feature extraction and classification. The experimental results obtained will be presented in the final paper for comparison purposes.

In this paper, we propose a Computer-Aided Detection (CAD) system based on the novel Multi-Classifier Fusion-based Classification Model (MCFM) of the breast lesions using textural features in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI). The proposed system consists of four principal phases: Region Of Interest (ROI) segmentation, feature extraction, fusion and selection and finally Classification. We have extracted a complementary set of features from the DCE-MRI database on two statistical descriptors such as the Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) methods. Then, we have concatenated the extracted features using Serial method to exploit the complementary between these sets. Next, we have applied a features selection phase through Principal Component Analysis (PCA) and Feature Subset Selection (FSS) techniques to remove redundant information and improve the overall performance of breast DCE-MRI in a computer-aided system. For the classification, we have used a set of classifiers (K-Nearest Neighbors (K-NN), Support Vector Machines (SVM) and Multilayer Perceptron-Artificial Neural Network (MLP-ANN)). Then, a fusion Model between them using several metrics is applied. Experimental validation is performed over an MRI dataset, which is comprised of 286 patients with 143 Masses and 143 No Masses (Normal) breast tissues. These experiments report encouraging performances for mass detection, achieved using ten-fold cross-validation model, in terms of the averaged measurements of different metrics such as Sensitivity, Specificity, Area Under Curve (AUC) and Accuracy, which are 0.8972, 0.9572, 0.9630 and 0.9501, respectively. The proposed DCE-MRI analysis model can offer an efficient system for radiologists to identify the breast tissues.

This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high- and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.

Brain–computer interface (BCI) technology uses electrophysiological (EEG) signals to detect user intent. Research on BCI has seen rapid advancement, with researchers proposing and implementing several signal processing and machine learning approaches for use in different contexts. BCI technology is also used in neuromarketing to study the brain’s responses to marketing stimuli. This study sought to detect two preference states (like and dislike) in EEG neuromarketing data using the proposed EEG-based consumer preference recognition system. This study investigated the role of feature selection in BCI to improve the accuracy of preference detection for neuromarketing. Several feature selection methods were used for benchmark testing in multiple BCI studies. Four feature selection approaches, namely, principal component analysis (PCA), minimum redundancy maximum relevance (mRMR), recursive feature elimination (RFE), and ReliefF, were used with five different classifiers: deep neural network (DNN), support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forest (RF). The four approaches were compared to evaluate the importance of feature selection. Moreover, the performance of classification algorithms was evaluated before and after feature selection. It was found that feature selection for EEG signals improves the performance of all classifiers.

Rigorous modeling of CO2 equilibrium absorption in ionic liquids