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Analysis of mental arithmetic based electroencephalography (EEG) signal can be helpful for patients who have difficulty learning or understanding arithmetic or have autism spectrum disorders. It is difficult to separate mental arithmetic from EEG signals since these signals are nonstatic and nonlinear. In this study, we extracted features based entropy, skewness and entropy + skewness from the EEG...
In this paper, we proposed an optimum combination of sub-band power features method for improving the classification accuracy rate of left- or right-hand movement imagery electroencephalogram signals. The sub-band power features were extracted from the best time segment of electroencephalogram trials and the proposed training model determined the optimum combination of sub-bands. Our approach was...
Brain-computer interfaces allow people to manage electronic devices such as computers without using their motor nervous system. When the brain is in a function, nerve cells in the brain communicate with each other with electrochemical interactions. Electroencephalogram (EEG) signals are recorded with the aid of electrodes during this function of the brain. These signals enable interaction between...
Nowadays, modern prostheses, which is defined as myoelectrical controllers, are used that can be controlled by using signals from muscles instead of traditional prostheses. In this paper, the electromyography (EMG) signals recorded when ten finger movements including one finger, two fingers and hand close movements were performed are classified using features by statistical methods. EMG signals were...
Brain computer interface applications have big importance in becoming a bridge between the human brain and devices. The studies in this area increase every day with the use of different feature extractions and classification methods In this study, classification is done by Random Forest method using Data Set III presented in BCI Competiton 2003, and it has been shown that combining the Fast Walsh...
Brain computer interface systems are modeled to facilitate lives of patients who have not a problem in their cognitive functions but also can not move their muscles. The performance of such systems highly depends on features extracted from the Electrocorticography (ECoG) signals, selected classifiers for features and channels of ECoG signals. In this study, we proposed a novel method which provides...
A lot of information can be attained with analysing biological signals which are electroencephalogram, electrocardiogram, electromyogram, magnetoencephalogram and photoplethysmography (PPG). This information is utilizez for in both diagnosis and criminal research. Photoplethysmography is a painless, simple and inexpensive optical technique that can be used to detect blood volume changes in microvascular...
The human brain, nerve center of command system, receives stimulus from the sense organs and sends these signals out to the muscles. There are many kinds of techniques about watching answer the brain for inputs coming from the sense organs. Functional magnetic resonance imaging, electrocorticography, magnetoencephalography and electroencephalography (EEG) techniques are frequently used to measure...
The responses of the brain into different information coming from sense organs could be analyzed by various kinds of measuring techniques. Among the existing techniques, Electroencephalography (EEG) is widely used because of its low setup costs, easy implementation and noninvasive nature. The response of the human brain to olfaction has been analyzed in recent years. Particularly, it has not been...
Brain computer interface (BCI) allows people to communicate with machines without the use of muscle systems. Although there are various kind of techniques to understand intend of the BCI user, electroencephalography (EEG) is the most popular, practical and widely implemented one. The performance of the EEG based BCI highly depends on extracting effective features. However, there is no a general feature...
The human brain, which receives input from the sensory organs and sends output to the muscles, is the command center of the nervous system. There are various kinds of brain monitoring techniques including computed tomography, magnetic resonance imaging (MRI), positron emission tomography, functional MRI, electroencephalography (EEG) and magnetoencephalography. Among those of techniques EEG is the...
Electroencephalogram (EEG), which is widely used for brain computer interface (BCI) systems for input signal, is easily interrupted by physical or mental tasks, and contaminated with various artifacts including power line noise, electromyogram and electrocardiogram. Therefore, such kind of artifacts cause to decrease the accuracy rate and motivate the researchers substantially develop the performance...
The human brain is probably the most amazing and complex system in universe. With approximately hundred billion neurons, brain transforms a variety of inputs such as thought, feeling, sound and odor into a physical or emotional response. A way to measure the response of the brain into such inputs is analyzing the electroencephalogram (EEG) signals. In this work, the EEG signals, which were recorded...
Feature extraction is a very crucial step at modern electroencephalogram (EEG) based brain computer interface system. Various feature extraction techniques have been proposed in order to represent EEG signals. With this study, it was shown that the classification accuracy increased by extracting features from different time segment of EEG signals. The proposed method improved the average classification...
The studies in brain computer interface field generally deal with about either improving the performance of their works or investigating new applications. In this study, it is aimed to improve the effectiveness of common spatial pattern features by determining the optimum duration of electroencephalogram based brain computer interface trials. With the proposed method it is shown that the classification...
Motivation of subject who associated electroencephalogram based brain computer interface experiments is one of the most important parameters which affect this kind of research's performance. Researchers can not able to measure how the motivation of subject exactly changes during the experiment. The proposed method was successfully applied to the BCI Competition 2003 Data Set III by using discrete...
Input signals of an EEG based brain computer interface (BCI) system are naturally non-stationary, have poor signal to noise ratio, depend on physical or mental tasks and are contaminated with various artifacts such as external electromagnetic waves, electromyogram and electrooculogram. All these disadvantages have motivated researchers to substantially improve speed and accuracy of all components...
Brain computer interface technology comes at the beginning of the popular study subject for scientist that of excite all of humanity. By means of that technology it is allowed to control electronic devices for paralyzed or partial paralysis humans to make their lives easier. In literature there have been many cursor movement imagery studies based on electroencephalogram (EEG) signals. However, the...
Feature extraction is a very challenging task because the choice of discriminative features directly affects the classification performance of brain computer interface system. The objective of this paper is to investigate the Mother Wavelets' affects on classification results. In order to execute this, we extracted features from three different data sets by using twelve Mother Wavelets. Then we classified...
In this paper a new algorithm to calculate optimum value of k for k-nearest neighborhood (k-NN) is proposed. Selection of k value is very important in k-NN classification algorithm. Our algorithm applied to sub-sampling and K-fold cross validation methods, separately. We applied our algorithm in different distribution of data set with different variances and means. We compared our algorithm with other...
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