The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Automatic analysis of cardiac arrhythmias is very important for diagnosis of cardiac abnormities. This paper presents a novel approach that classifies ECG signals with the combination of Wavelet transform and Decision tree classification. This approach has two aspects. In the first aspect, we utilize the wavelet transform to extract the ECG signals wavelet coefficients as the first features and utilize...
Electroencephalogram (EEG) signals are often contaminated by ocular artifacts. In present study, a novel and robust technique is presented to eliminate ocular artifacts from EEG signals automatically. Independent component analysis (ICA) method is used to decompose EEG signals. In the first step, the features of topography and power spectral density of those components are extracted. In the second...
Always, one of the issues in the brain-computer interface (BCI) is to extract components from raw EEG data that have more information in order to separate task-related potentials from other neural and artifactual EEG sources. In this paper, a new method is proposed for extracting components from raw EEG data such that these components have maximal information for separating task-related potentials...
One of the preprocessors can be used to improve the performance of brain-computer interface (BCI) systems is independent component analysis (ICA). ICA is a signal processing technique in which observed random data are transformed into components that are statistically independent from each other. This suggests the possibility of using ICA to separate different independent brain activities during motor...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.