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Feature selection represents a key stage in electroencephalogram (EEG) classifications, because these applications involve numerous, high-dimensional samples. In recent literature, a multitude of supervised embedded feature selection procedures has been proposed. Regardless if they are configured as Single Objective (SOO) or Multi-Objective Optimizations (MOO), the embedded methods assess the quality...
In this study, EEG data recorded during mental arithmetic operations and silent reading were analyzed by discrete wavelet transform and feature vectors were obtained. The obtained feature vectors are classified by Support Vector Machines (SVM). Results are given for 26 channels, all recorded channels, and for 10 most effective channels. Correlation based feature selection based algorithm is used for...
In this study, a new approach for estimation of Obstructive Sleep Apnea Syndrome (OSAS) was proposed. OSAS is a sleep disorder that affects the life comfortability in human life. Diagnosis of OSAS is usually done by expensive devices and specialist physicians. Since OSAS is serious, it should be diagnosed and treated early. In this study, a new feature extraction method is proposed for OSAS diagnosis...
Epileptic seizure is one of the most common neurological diseases around the world. It is clinical symptoms and/or signs due to abnormal excessive or synchronous neuronal activity in the human brain. Electroencephalogram (EEG) that measures the electrical activity of the brain generated by the cerebral cortex nerve cells, is the most utilized test to detect the seizure activities by visual scanning...
The discrimination of the preictal state in EEG signals is of great importance in neuroscience and the epileptic seizure prediction field has yet to provide conclusive evidence. In this study, three different classification approaches, including the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm, Support Vector Machines (SVMs) and Neural Networks (NNs), are investigated...
This study investigates the discrimination between calm, exciting positive and exciting negative emotional states using EEG signals. Towards this direction, a publicly available dataset from eNTERFACE Workshop 2006 was used having as stimuli emotionally evocative images. At first, EEG features were extracted based on literature review. Then, a computational framework is proposed using machine learning...
With the evolution of technology and the major role that technology now plays in the diagnosis and identification of disorders and difficulties, improving the accuracy of diagnostic systems is paramount. Improving and evaluating the way in which patterns of results are identified and classified may help uncover answers that are not always obvious. This paper attempts to discover such patterns found...
In this paper, the single-channel EEG based classification systems using simple extracted features are investigated. Each classification system contains the following stages: data acquisition, signal decomposition, feature extraction, and classification. In addition to using the filter bank and empirical mode decomposition (EMD) methods for signal decomposition, a sparse discrete wavelet packet transform...
Assessing a mental workload level using electroencephalography (EEG) signals represents an active research area. The development of low-cost wireless EEG headsets drew the attention of researchers in the field of critical human-machine collaboration systems. In this paper, some classification methods are used to discriminate the working memory load levels using EEG raw data records. The brain waves...
Emotion plays an important role in human daily life and is a significant feature for interaction among people. Due to having adaptive role, it motivate human to respond stimuli in their environment quickly for improving their communication, learning and decision-making. With increasing role of brain computer interface (BCI) in interaction between users and computer, automatic emotion recognition has...
Human brain has a complex structure with the billions of neurons, so it is a difficult and challenging task to predict the behavior of human brain. Different methods and classifiers are used to measure and classify the brain activities with higher accuracy and reliability. In this paper, instead of using mostly used classifier (support vector machine), prediction of the brain activity is done by estimating...
Brain Computer Interface (BCI) technology is used to help patients who do not have control over motor neurons such as ALS or paralyzed patients, to communicate with outer world. This work aims to classify motor imageries using real-time EEG dataset, which was published by Graz University, Austria. The dataset consists of two-channel EEG signals of right-hand movement imagery and left-hand movement...
Mental state classification is an important step for realizing a control system based on electroencephalography (EEG) signals which could benefit a lot of paralyzed people including the locked-in or Amyotrophic Lateral Sclerosis. Considering that EEG signals are nonstationary and often contaminated by various types of artifacts, classifying thoughts into correct mental states is not a trivial problem...
This paper studies the supervised classification of electroencephalogram (EEG) brain signals to identify persons and their activities. The brain signals are obtained from a commercially available and modestly priced wearable headband. Such wearable devices generate a large amount of data and due to their attractive pricing structure are becoming increasingly commonplace. As a result, the data generated...
The traditional E-learning system is limited with monitoring attention level of students. The online instructor cannot monitor whether the students remain focus during online autonomous learning. Along with the attention, emotions are also intrinsically related to the way that individuals interact with each other as well as machines. The behavior and emotions can be better understood by a human being...
Extreme Learning Machine (ELM) is a fast and efficient classifier with single hidden layer feed-forward neural networks. In this paper, the ELM is employed to classify the EEG signals in BCI system, the BCI competition datasets are used to test, the mutual information and classify accuracy are considered as evaluation criteria. Compare with the LDA and SVM, the ELM method could obtain more mutual...
A robust model is sought for the identification of electroencephalographic (EEG) signals including movements of three distinct parts of the user's arm, namely hand, elbow and shoulder. This study investigates the classification performances of the same upper limb motor movements using various kernel functions of the support vector machine (SVM). Polynomial, linear and radial basis (RBF) functions...
Human brain is considered as complex system having different mental states e.g., rest, active or cognitive states. It is well understood fact that brain activity increases with the cognitive load. This paper describes the cognitive and resting state classification based on EEG features. Previously, most of the studies used linear features. EEG signals are non-stationary in nature and have complex...
The purpose of this paper is to analyze the electroencephalography (EEG) signals of human brain during the 2D and 3D video watching. Analysis of the visual 2D and 3D motion pictures is meaningful and could be used in brain-computer interface and maybe detection of drowsiness. A total of eight healthy subjects consist of three women and five men that were in their twenties attended in the clinical...
User authentication is crucial in security systems. Although, there are many complex and secure passkey-based authentication mechanisms, majority of users prefer employing simple passwords that are viable to rubber-hose attacks. Image sequence based passwords were introduced to overcome some of the issues with textual passwords. The objective of this work was to evaluate cognitive and memory performance...
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