ELECTROENCEPHALOGRAM CLASSIFICATION BY FORECASTING WITH RECURRENT NEURAL NETWORKS The ability to effectively classify electroencephalograms (EEG) is the foundation for building usable Brain-Computer Interfaces as well as improving the performance of EEG analysis software used in clinical and research settings. Although a number of research groups have demonstrated the feasibility of EEG classif...

Brain Electroencephalography (EEG) classification is widely applied to analyze cerebral diseases in recent years. Unfortunately, invalid/noisy EEGs degrade the diagnosis performance and most previously developed methods ignore the necessity of EEG selection for classification. To this end, this paper proposes a novel maximum weight clique-based EEG selection approach, named mwcEEGs, to map EEG ...

Electroencephalograph (EEG) signals feature extraction and processing is one of the most difficult and important part in the brain-computer interface (BCI) research field. EEG signals are generally unstable, complex and have low signal-noise ratio, which is difficult to be analyzed and processed. To solve this problem, this paper disassembles EEG signals with the empirical mode decomposition (E...

OBJECTIVE In this paper, we have done Bayes Discriminant analysis to EEG data of experiment objects which are recorded impersonally come up with a relatively accurate method used in feature extraction and classification decisions. METHODS In accordance with the strength of α wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we have...

how to cite this article: asadi-pooya aa, emami m. is interictal eeg correlated with the seizure type in idiopathic (genetic) generalized epilepsies? iran j child neurol 2012;6(2): 25-28.   objective we investigated the correlation between different interictal eeg abnormalities observed in patients with idiopathic (genetic) generalized epilepsies (iges) and their seizure types. material & metho...

The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary c...

This paper presents a method for automatically selecting the optimal EEG rhythm/channel combination capable of classifying the different human alertness states. We considered four alertness states, namely ’engaged’, ’calm’, ’drowsy’, and ’asleep’. Energies associated with the conventional EEG rhythms, δ, θ, α, β and γ, extracted from overlapping segments of the different EEG channels were used ...