Standing up for the body. Recent progress in uncovering the networks involved in the perception of bodies and bodily expressions.

Recent studies of monkeys and humans have identified several brain regions that respond to bodies. Researchers have so far mainly addressed the same questions about bodies and bodily expressions that are already familiar from three decades of face and facial expression studies. Our present goal is to review behavioral, electrophysiological and neurofunctional studies on whole body and bodily expression perception against the background of what is known about face perception. We review all currently available evidence in more detail than done so far, but we also argue for a more theoretically motivated comparison of faces and bodies that reflects some broader concerns than only modularity or category specificity of faces or bodies

Classification of epilepsy seizure phase using interval type-2 fuzzy support vector machines

An interval type-2 fuzzy support vector machine (IT2FSVM) is proposed to solve a classification problem which aims to classify three epileptic seizure phases (seizure-free, pre-seizure and seizure) from the electroencephalogram (EEG) captured from patients with neurological disorder symptoms. The effectiveness of the IT2FSVM classifier is evaluated based on a set of EEG samples which are collected from 10 patients at Peking university hospital. The EEG samples for the three seizure phases were captured by the 112 2-s 19 channel EEG epochs, where each patient was extracted for each sample. Feature extraction was used to reduce the feature vector of the EEG samples to 45 elements and the EEG samples with the reduced features are used for training the IT2FSVM classifier. The classification results obtained by the IT2FSVM are compared with three traditional classifiers namely Support Vector Machine, k-Nearest Neighbor and naive Bayes. The experimental results show that the IT2FSVM classifier is able to achieve superior learning capabilities with respect to the uncontaminated samples when compared with the three classifiers. In order to validate the level of robustness of the IT2FSVM, the original EEG samples are contaminated with Gaussian white noise at levels of 0.05, 0.1, 0.2 and 0.5. The simulation results show that the IT2FSVM classifier outperforms the traditional classifiers under the original dataset and also shows a high level of robustness when compared to the traditional classifiers with white Gaussian noise applied to it