Mohammad H. Alomari

Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning

In this paper, we propose an automated computer platform for the purpose of classifying Electroencephalography (EEG) signals associated with left and right hand movements using a hybrid system that uses advanced feature extraction techniques and machine learning algorithms. It is known that EEG represents the brain activity by the electrical voltage fluctuations along the scalp, and Brain-Computer Interface (BCI) is a device that enables the use of the brains neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements. In our research work, we aspired to find the best feature extraction method that enables the differentiation between left and right executed fist movements through various classification algorithms. The EEG dataset used in this research was created and contributed to PhysioNet by the developers of the BCI2000 instrumentation system. Data was preprocessed using the EEGLAB MATLAB toolbox and artifacts removal was done using AAR. Data was epoched on the basis of Event-Related (De) Synchronization (ERD/ERS) and movement-related cortical potentials (MRCP) features. Mu/beta rhythms were isolated for the ERD/ERS analysis and delta rhythms were isolated for the MRCP analysis. The Independent Component Analysis (ICA) spatial filter was applied on related channels for noise reduction and isolation of both artifactually and neutrally generated EEG sources. The final feature vector included the ERD, ERS, and MRCP features in addition to the mean, power and energy of the activations of the resulting Independent Components (ICs) of the epoched feature datasets. The datasets were inputted into two machine- learning algorithms: Neural Networks (NNs) and Support Vector Machines (SVMs). Intensive experiments were carried out and optimum classification performances of 89.8 and 97.1 were obtained using NN and SVM, respectively. This research shows that this method of feature extraction holds some promise for the classification of various pairs of motor movements, which can be used in a BCI context to mentally control a computer or machine. Keywords—EEG; BCI; ICA; MRCP; ERD/ERS; machine learning; NN; SVM

Wavelet-Based Feature Extraction for the Analysis of EEG Signals Associated with Imagined Fists and Feet Movements

Electroencephalography (EEG) signals were analyzed in many research applications as a channel of communication between humans and computers. EEG signals associated with imagined fists and feet movements were filtered and processed using wavelet transform analysis for feature extraction. The proposed work used Neural Networks (NNs) as a classifier that enables the classification of imagined movements into either fists or feet. Wavelet families such as Daubechies, Symlets, and Coiflets wavelets were used to analyze the extracted events and then different feature extraction measures were calculated for three detail levels of the wavelet coefficients. Intensive NN training and testing experiments were carried out and different network configurations were compared. The optimum classification performance of 89.11% was achieved with a NN classifier of 20 hidden layers while using the Mean Absolute Value (MAV) of the Coiflets wavelet coefficients as inputs to NN. The proposed system showed a good performance that enables controlling computer applications via imagined fists and feet movements.

EEG Mouse:A Machine Learning-Based Brain Computer Interface

The main idea of the current work is to use a wireless Electroencephalography (EEG) headset as a remote control for the mouse cursor of a personal computer. The proposed system uses EEG signals as a communication link between brains and computers. Signal records obtained from the PhysioNet EEG dataset were analyzed using the Coif lets wavelets and many features were extracted using different amplitude estimators for the wavelet coefficients. The extracted features were inputted into machine learning algorithms to generate the decision rules required for our application. The suggested real time implementation of the system was tested and very good performance was achieved. This system could be helpful for disabled people as they can control computer applications via the imagination of fists and feet movements in addition to closing eyes for a short period of time. Keywords—EEG; BCI; Data Mining; Machine Learning; SVMs; NNs; DWT; Feature Extraction

Application of Wavelet Transform Analysis to ADCs Harmonics Distortion

This paper presents a new method of detecting Analog to Digital Converter harmonic distortion. The new method is based on Wavelet multi-resolution process to identify instantaneous harmonic components. In classical testing, Fourier transform algorithm was long adopted to estimate Total Harmonic Distortion by obtaining signal power spectrum. While the conventional method of Fourier transform tend to be complicated and lengthy, the new investigated algorithms of Wavelet transform has shown less computations process and instantaneous testing of ADCs harmonic distortions. By shorten testing times and reduced computation complexity, Wavelet transform can be particularly appropriate for developing ADCs low-cost, and fast testing procedure.

Using the real, gentle and modest AdaBoost learning algorithms to investigate the computerised associations between Coronal Mass Ejections and filaments

Space weather forecasting is a very challenging task and investigating the associations between properties (i.e., shape, scale, location) of the related solar features, appearing in solar images, are usually complicated because of the variation in their physical and visual properties. Establishing the correlations among the occurrences of solar activities and solar features is a long-standing problem in solar imaging. This work is an attempt to shed more light on the driving forces behind the initiations of Coronal Mass Ejections (CMEs). This is still a big mystery in this field and in this work we have analysed years of data relating to one particular feature, filaments, to determine if an association between filaments and the eruptions of CMEs can be drawn. The resulting association set has been fed to a powerful machine learning algorithm to determine if CMEs can be predicted solely based on filaments. Our learning algorithm, AdaBoost, is used because of robust and accurate performance. Three of the most common versions of the Adaboost algorithm are used in this work, which are the Gentle AdaBoost, the Real AdaBoost and the Modest AdaBoost.

Morphological-Based Filtering of Noise: Practical Study on Solar Images

In this paper, a morphological-based algorithm is proposed for noise filtering in digital images. This algorithm is based on the morphological hit-miss transform (HMT). It is applied on a real-life problem, which is the detection of solar features in H-alpha solar images that are obtained from Meudon Observatory. These images are processed by the automated detection system of Filaments reported by R. Qahwaji and T. Colak [1]. The automated detection system works well when detecting filaments in noise-free solar images; it achieves false acceptance rate (FAR) error rate of 4% and false rejection rate (FRR) error rate of 36% when compared with the manually detected filaments in the synoptic maps. When the detection is applied after the addition of Gaussian noise to the solar images it achieves FAR of 3% and FRR of 51%. Then by filtering using the proposed algorithm, the detection performance is enhanced to achieve FAR of 8% and FRR of 13%.

SYSTEMS VARIABILITY MODELING: A TEXTUAL MODEL MIXING CLASS AND FEATURE CONCEPTS

System’s reusability and cost are very important in software product line design area. Developers’ goal is to increase system reusability and decreasing cost and efforts for building components from scratch for each software configuration. This can be reached by developing software product line (SPL). To handle SPL engineering process, several approaches with several techniques were developed. One of these approaches is called separated approach. It requires separating the commonalities and variability for system’s components to allow configuration selection based on user defined features. Textual notationbased approaches have been used for their formal syntax and semantics to represent system features and implementations. But these approaches are still weak in mixing features (conceptual level) and classes (physical level) that guarantee smooth and automatic configuration generation for software releases. The absence of methodology supporting the mixing process is a real weakness. In this paper, we enhanced SPL’s reusability by introducing some meta-features, classified according to their functionalities. As a first consequence, mixing class and feature concepts is supported in a simple way using class interfaces and inherent features for smooth move from feature model to class model. And as a second consequence, the mixing process is supported by a textual design and implementation methodology, mixing class and feature models by combining their concepts in a single language. The supported configuration generation process is simple, coherent, and complete.

A Predictive Model for Solar Photovoltaic Power using the Levenberg-Marquardt and Bayesian Regularization Algorithms and Real-Time Weather Data

The stability of power production in photovoltaics (PV) power plants is an important issue for large-scale gridconnected systems. This is because it affects the control and operation of the electrical grid. An efficient forecasting model is proposed in this paper to predict the next-day solar photovoltaic power using the Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms and real-time weather data. The correlations between the global solar irradiance, temperature, solar photovoltaic power, and the time of the year were studied to extract the knowledge from the available historical data for the purpose of developing a real-time prediction system. The solar PV generated power data were extracted from the power plant installed on-top of the faculty of engineering building at Applied Science Private University (ASU), Amman, Jordan and weather data with real-time records were measured by ASU weather station at the same university campus. Huge amounts of training, validation, and testing experiments were carried out on the available records to optimize the Neural Networks (NN) configurations and compare the performance of the LM and BR algorithms with different sets and combinations of weather data. Promising results were obtained with an excellent realtime overall performance for next-day forecasting with a Root Mean Square Error (RMSE) value of 0.0706 using the Bayesian regularization algorithm with 28 hidden layers and all weather inputs. The Levenberg-Marquardt algorithm provided a 0.0753 RMSE using 23 hidden layers for the same set of learning inputs. This research shows that the Bayesian regularization algorithm outperforms the reported real-time prediction systems for the PV power production.

Next-Day Prediction of Sunspots Area and McIntosh Classifications Using Hidden Markov Models

In this paper, Hidden Markov Models (HMMs) are used to study the evolution of sunspots and to develop a model that can be used to predict the McIntosh class and the sunspot area for the sunspot under investigation for the next 24 hours. The testing results show accuracy in the prediction of next-day area and McIntosh classification reaching up to 71% and 60% respectively, when studied on the period from 18/08/1996 till 31/03/2006.

Support Vector Machines for automated knowledge extraction from historical solar data: A practical study on CME predictions

In this paper, Associations algorithms and Support Vector Machines (SVM) are applied to analyse years of solar catalogues data and to study the associations between eruptive filaments/prominences and Coronal Mass Ejections (CMEs). The aim is to identify patterns of associations that can be represented using SVM learning rules to enable real-time and reliable CME predictions. The NGDC filaments catalogue and the SOHO/LASCO CMEs catalogue are processed to associate filaments with CMEs based on timing and location information. Automated systems are created to process and associate years of filaments and CME data, which are later arranged in numerical training vectors and fed to machine learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Features representing the filament time, duration, type and extent are extracted from all the associated (A) and not-associated (NA) filaments and converted to a numerical format that is suitable for machine learning use. The machine learning system predicts if the filament is likely to initiate a CME. Intensive experiments are carried out to optimise the SVM. The prediction performance of SVM is analysed and recommendations for enhancing the performance are provided.