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In this study, EEG records taken from healthy people with eyes open and eyes closed, EEG records taken from epileptic patients at the time of seizure and out of seizure were classified using Naive Bayes, K-Nearest Neighbor and Artificial Neural Networks methods. Feature vectors are obtained by using Daubechies wavelet transforms with different degrees and their effect on the classification success...
In this article we present a fusion architecture for the automatic classification of sleep stages. The architecture relies on time and frequency domain features which are processed by dissimilar classifiers. The initial predictions of each classifier are refined by using fusion of the prediction estimations together with temporal contextual information of the electroencephalographic signal. The experimental...
We present an accurate seizure detection algorithm, and make a detailed comparison of two frequency analysis methods: a widely used stationary method — Fast Fourier Transform (FFT) and a relatively new nonstationary method — Hilbert-Huang Transform (HHT). Two public databases and one our own database were tested. The results show that our algorithm has very high accuracy compared with the state-of-the-art...
The aim of this study is to evaluate a new method for seizure detection using the tripolar Laplacian electroence-phalography signal (tEEG) recorded using a tripolar concentric ring electrode (TCRE) on the scalp surface of rats based on empirical mode decomposition (EMD) and time-frequency energy concentration. Data from 10 rats were examined with the proposed algorithm. After EMD decomposition, three...
This study examines the feasibility of online detection of tremor-related component in noninvasively acquired multichannel electroencephalographic (EEG) signals. In particular, performances of different feature extraction techniques, ranging from time-frequency and time-scale analysis to blind source separation of EEG signals are mutually compared and their suitability for online tremor detection...
Past studies reported that the main electrogastrography (EEG) dynamic changes related to motion sickness (MS) were occurred in occipital, parietal, and somatosensory brain area, especially in the power increasing of the alpha band (8-13 Hz) and theta band (4-7 Hz) which had positive correlation with the subjective MS level. Depend on these main findings correlated with MS, we attempt to develop an...
Sleep staging has an effective role in diagnosing sleep disorders. Sleep staging is generally done by a sleep expert through examining Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG) signals of the patients and determining the stages of sleep in different time sections. This type of sleep staging is preferred among the sleep experts but because it is rather tiring and time...
In order to improve the accuracy of the brain state classification, the time-frequency-spatial filter algorithm is put forward. In this algorithm, the signal features are extracted in terms of time, frequency and space. The parameters of the spatial pattern filter are not fixed, but variable with time. Specific frequency bands are also optimized simultaneously in this algorithm. So it can perfect...
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