One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to interand intra-subject differences, as well as the inherent noise associated with EEG data collection. Herein, we explore the capabilities of the recent deep neural architectures for modeling cognitive events from EEG data. In this paper, we present recent ach...

Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken i...

The contribution presents an application of a movement-related EEG temporal development classification which improves the classification score of voluntary movements controlled by closely localized regions of the brain. A dynamic Hidden Markov Model-based (HMM) classifier specifically designed to capture EEG temporal behavior was used. Surprisingly, HMM classifiers are rarely used for BCI desig...

Traditional analysis methods for single-trial classification of electroencephalography (EEG) focus on two types of paradigms: phase locked methods, in which the amplitude of the signal is used as the feature for classification, e.g. event related potentials; and second order methods, in which the feature of interest is the power of the signal, e.g. event related (de)synchronization. The procedu...

Deep learning, and in particular Deep Belief Network (DBN), has recently witnessed increased attention from researchers as a new classification platform. It has been successfully applied to a number of classification problems, such as image classification, speech recognition and natural language processing. However, deep learning has not been fully explored in electroencephalogram (EEG) classif...

Epilepsy is common neurological disorder disease in the world. Electroencephalogram (EEG) can provide significant information about epileptic activity in human brain. Since detection of the epileptic activity requires analyzing of very length EEG recordings by an expert, researchers tend to improve automated diagnostic systems for epilepsy in recent years. In this work, we try to automate detec...

Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor calle...

Identifying the vigilance states of the mammalian is an important research topic to bioscience in recently years, which the vigilance states is usually categorized as slow wave sleep, rapid eye movement sleep, and awake, etc. To discriminate difference vigilance states, a well-trained expert needs spend a long time to analyze a mass of physiological record data. In this paper, we proposed an au...

This paper describes the pattern recognition technique based on multiscale discrete wavelet transform(MDWT) and least square support vector machine (LS-SVM) for the classification of EEG signals. The different statistical features are extracted from each EEG signal corresponding to various seizer and nonsiezer brain functions, using MDWT. Further these sets of features are fed to the LS-SVM mul...

This study explored whether we could accurately classify perceived and imagined musical stimuli from EEG data. Successful EEG-based classification of what an individual is imagining could pave the way for novel communication techniques, such as brain-computer interfaces. We recorded EEG with a 64-channel BioSemi system while participants heard or imagined different musical stimuli. Using princi...