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Classification methods typically make use only of labeled data, in what is known as supervised learning. In some applications, however, labeled data is either scarce or costly to obtain. For these applications, unsupervised or semisupervised learning are adequate, since they will be able to use unlabeled data. This work proposes a new method for unsupervised and semisupervised learning of non-Gaussian...
The recording of brain activity at the scalp level, also known as electroencephalography (EEG), is a brain imaging technique commonly used in the clinical environment. Adequate modeling of the recorded signals could help to improve the diagnosis of several illnesses such as sleep disorders and epilepsy. This paper presents a computational cost analysis for dynamic modeling methods and considers their...
This paper presents a study of the application of new variants of the Sequential Independent Analysis Mixture Models (SICAMM) to the modeling and classification of electroencephalographic (EEG) signals. The real application approached was the detection of microarousals in EEG signals from sleep apnea patients. In addition, the proposed methods were tested on synthetic data with probability density...
Independent Component Analysis (ICA) is a blind source separation method that has proven popular in many fields of application. ICA can be improved incorporating temporal dependencies creating dynamic ICA methods and defining subspaces with multiple ICAs. Such a dynamic ICA method is called Sequential Independent Component Analysis Mixture Model (SICAMM). This method is proposed for two new EEG signal...
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