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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...
Sleep staging is one of the important areas which is used to diagnose several diseases. People try to obtain models to carry out this operation without human interaction due to the time-consuming and complex nature of classification process. Most of the prior studies use concatenation of the extracted features from the electroencephalography (EEG) signals to obtain a single classifier. However, concatenating...
Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product...
EEG is one of the biomarkers adequate for memory load assessment. Feature Selection (FS) routines for electroencephalogram (EEG) signals have been extensively studied in the past years. Current research is often based on machine learning algorithms. This paper investigates the impact of a new evolutionary approach to Multi-Objective Optimization (MOO) of FS routine for memory load classification using...
This study aims to clarify whether classification accuracy between major depressive disorder (MDD) and healthy subjects increases with complementary use of graph theoretical measures to functional connectivity. Electroencephalography (EEG) signals were recorded from 37 unmedicated MDD subjects (21 female, 16 male) and 37 gender and age matched control subjects. Signals were recorded during eyes-closed...
One of the pivotal issues which must be tackled when an effective brain-computer interface (BCI) is to be designed, is to reduce the enormous space of features extracted from fNIRS signals. BCI researchers often use genetic algorithms (GA) as the technique to extract features. The classic genetic algorithm obtains a feature set with the high classification accuracy; however, it is unable to create...
In this paper, we show that it is possible to use electroencephalography (EEG) and multi-brain computing with two humans to guide an Interactive Genetic Algorithm (IGA) system. We show that combining neural activity across two brains increases accuracy to guide evolutionary search more effectively. The IGA system involves a simple task of evolving a polygon shape to approximate the shape of a target...
EEG signals usually have a high dimensionality which makes it difficult for classifiers to learn the difference of various classes in the underlying pattern in the signal. This paper investigates several evolutionary algorithms used to reduce the dimensionality of the data. The study presents electrode and feature reduction methods based on Genetic Algorithms (GA) and Particle Swarm Optimization (PSO)...
The brain signals are generally measured by Electroencephalogram (EEG) in Brain-Computer Interface (BCI) applications. In motor imagery-based BCI, the performed MI tasks (e.g., imagined hand movement) are identified through a classification algorithm to communicate and control the device. Consequently, improving the performance of the classifier is crucial to the success of the BCI system. One of...
The reduction of the number of EEG features to give as inputs to epilepsy seizure predictors is a needed step towards the development of a transportable device for real-time warning. This paper presents a comparative study of three feature selection methods, based on Support Vector Machines. Minimum-Redundancy Maximum-Relevance, Recursive Feature Elimination, Genetic Algorithms, show that, for three...
Wepilet is a series of novel orthogonal wavelets optimized for Electroencephalogram (EEG) signals, specialized for epileptic seizure prediction. The main idea is to design a mother wavelet that when applied to EEG signal to create the feature space, should enable a better classification of the brain state. Wepilet is developed by an iterative optimization process, employing Genetic Algorithm (GA)...
Epileptic seizure prediction, with at least some minutes in advance, would improve substantially the quality of life of patients with refractory epilepsy. This is addressed as a classification problem were the brain state is classified using a number of features extracted from the EEG. Methods based on computational intelligence, like support vector machines (SVM), are applied to build up classifiers...
Depression is one of the most common mental disorder that at its worst can lead to suicide. Diagnosing depression in the early curable stage is very important. In this paper we study performance of different classification techniques for classifying depression patients from normal subjects. For this aim, power spectrum of three frequency band (alpha, beta, theta) and the whole bands of EEG are used...
P300 spellers are mainly composed of an interface, by which alphanumerical characters are presented to users, and a classification system, which identifies the target character by using acquired EEG data. In this study, we proposed modifications both to the interface and to the classification system, in order to reduce the number of required stimulus repetitions and consequently boost the information...
This paper proposes an emotional stress recognition system with EEG signals using higher order spectra (HOS). A visual induction based acquisition protocol is designed for recording the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) under two emotional stress states of participants, Calm neutral and Negatively exited. After pre-processing the signals, higher order spectra are employed to extract...
A Brain Computer Interface is a system that provides an artificial communication between the human brain and the external world. The paradigm based on event related evoked potentials is used in this work. Our main goal was to efficiently solve a binary classification problem: presence or absence of P300 in the registers. Genetic Algorithms and Support Vector Machines were used in a wrapper configuration...
Current non-invasive brain-computer interface (BCI) designs use as much electroencephalogram (EEG) features as possible rather than few well known motor-reactive features (e.g. rolandic mu-rhythm picked from C3 and C4 channels). Additionally, motor-reactive rhythms do not provide BCI control for every subject. Thus, a subject-specific feature set needs to be determined from a large feature space....
EEG based motor imagery is widely used in most practical self-paced brain-computer interface systems where the user conveys his/her intents at will whenever they wish to do so, and the system doesn't tell the user when to perform the mental tasks that convey their intents to the system. The most important step for the self-paced BCI system is to discriminate between characteristic mental activity...
In this paper, EEG signals of ten schizophrenic patients and ten age-matched control participants are analyzed with the objective of determining which frequency bands have the more discriminative information. Our signals are caught from 22 channels according to 10-20 international recording system. First, the frequency range of 0-72 Hz is divided to 18 non-overlap intervals. Then, for each channel,...
In this paper, we evaluate the significance of feature and channel selection on EEG classification. The selection process is performed by searching the feature/channel space using genetic algorithm, and evaluating the importance of subsets using a linear support vector machine classifier. Three approaches have been considered: (i) selecting a subset of features that will be used to represent a specified...
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