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Reconstruction of brain sources from magnetoencephalography and electroencephalography (M/EEG) data is a well known problem in the neuroengineering field. A inverse problem should be solved and several methods have been proposed. Low Resolution Electromagnetic Tomography (LORETA) and the different variations proposed as standardized LORETA (sLORETA) and the standardized weighted LORETA (swLORETA)...
Goal: We aim to evaluate the mechanisms underlying the neurovascular/metabolic coupling in the epileptogenic cortices of rats with chronic focal epilepsy. Methods: We performed and analyzed intracranial recordings obtained from the seizure-onset zones during ictal periods on epileptic rats, and then, used these data to fit a metabolically coupled balloon model. Normal rats undergoing forepaw stimulation...
Human emotion recognition (HER) allows the assessment of an affective state of a subject. Until recently, such emotional states were described in terms of discrete emotions, like happiness or contempt. In order to cover a high range of emotions, researchers in the field have introduced different dimensional spaces for emotion description that allow the characterization of affective states in terms...
In this work, it is proposed a technique for the feature extraction of electroencephalographic (EEG) signals for classification of mental tasks which is an important part in the development of Brain Computer Interfaces (BCI). The Empirical Mode Decomposition (EMD) is a method capable to process nonstationary and nonlinear signals as the EEG. This technique was applied in EEG signals of 7 subjects...
Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates...
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