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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...
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...
This paper presents an effective technique for solving the prediction problem presented in Event-1 of Physionet Challenge 2009. In this challenge, the prediction of occurrence of an Acute Hypotension Episode (AHE) is to be made (in patients receiving pressor medication) using clinical data and medical signals prior to the start of a forecast window. The technique proposed in this paper uses time domain...
In recent years Microelectrode recording (MER) analysis has proved to be a powerful localization tool of basal ganglia for Parkinson disease's treatment, especially the Subthalamic Nucleus (STN). In this paper, a signal-dependent method is presented for identification of the STN and other brain zones in Parkinsonian patients. The proposed method, refereed as optimal wavelet feature extraction method...
The paper describes a feature selection process applied to electrogastrogram (EGG) processing. The data set is formed by 42 EGG records from functional dyspeptic (FD) patients and 22 from healthy controls. A wrapper configuration classifier was implemented to discriminate between both classes. The aim of this work is to compare artificial neural networks (ANN) and support vector machines (SVM) when...
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...
This research is on presenting a new approach for cardiac arrhythmia disease classification. The proposed method combines both support vector machine (SVM) and genetic algorithm approaches. First, twenty two features from electrocardiogram signal are extracted. These features are obtained semiautomatically from time-voltage of R, S, T, P, Q features of an Electro Cardiagram signals. Genetic algorithm...
This paper addressed a novel scheme of training Support Vector Machines (SVMs) for automatic discrimination of the change of human walking patterns. In order to classify gait patterns with higher accuracy, the independent component analysis (ICA) algorithm was proposed as a new pre-processing technique to extract gait features for initiating the training set of SVM. The gait data of 30 young and 30...
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 study, nonlinear dynamics techniques toward detecting cardiac murmurs from phonocardiograms (PCG) are used. With this purpose, a methodology for tuning parameters (reconstruction delay -tau and embedding dimension -m) involved in the reconstruction of a meaningful state space from scalar time series is presented, using genetic algorithms (GA), as well as constructing a meta-algorithm combined...
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...
The electrocorticogram (ECoG) recorded from subdural electrodes is a kind of BCI signal source that has the potential to achieve good classification results. The feature extraction and its subset selection are crucial for increasing classification accuracy rate. This paper proposes a new algorithm for classifying single-trial ECoG during motor imagery. The nonlinear regressive coefficients between...
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...
The electrocorticogram (ECoG) recorded from subdural electrodes is a kind of BCI signal source that has the potential to achieve good classification results. The feature extraction and its subset selection are crucial for increasing classification accuracy rate. This paper proposes a new algorithm for classifying single-trial ECoG during motor imagery. The nonlinear regressive coefficients between...
A robot system controlled by human thoughts is introduced in this paper. Aiming at the recognition problem of electroencephalogram (EEG) signals in the system, we present a novel pattern recognition method. The method combines the genetic algorithm (GA) with the support vector machine (SVM). It includes two techniques. One is that the feature selection and model parameters of the SVM are optimized...
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