The electroencephalogram (EEG) is the most common tool used in sleep research. This unit describes the methods for recording and analyzing the EEG. Detailed protocols describe recorder calibration, electrode application, EEG recording, and computer EEG analysis with power spectral analysis. Computer digitization of an analog EEG signal is discussed, along with EEG filtering and the parameters o...

A wearable electroencephalogram (EEG) device for continuous monitoring of patients suffering from epilepsy would provide valuable information for the management of the disease. Currently no EEG setup is small and unobtrusive enough to be used in daily life. Recording behind the ear could prove to be a solution to a wearable EEG setup. This article examines the feasibility of recording epileptic...

............................................................................................................................... i ACKNOWLEDGEMENTS ......................................................................................................... iii LIST OF ORIGINAL PUBLICATIONS ........................................................................................ vii AUTHOR’S CONTRIBU...

Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG) based BCIs are promising solutions due to their convenient and portable instruments. Motor imagery EEG (MI-EEG) is a kind of most widely focused EEG signals, which reveals a subject’s movement intentions without actual acti...

The electroencephalograph (EEG) signal is one of the most widely signal used in the bioinformatics field due to its rich information about human tasks. In this work EEG waves classification is achieved using the Discrete Wavelet Transform DWT with Fast Fourier Transform (FFT) by adopting the normalized EEG data. The DWT is used as a classifier of the EEG wave’s frequencies, while FFT is impleme...

Brain-Computer interface systems are a new mode of communication which provides a new path between brain and its surrounding by processing EEG signals measured in different mental states.  Therefore, choosing suitable features is demanded for a good BCI communication. In this regard, one of the points to be considered is feature vector dimensionality. We present a method of feature reduction us...

AIM To assess the diagnostic value of amplitude-integrated electroencephalography (EEG) in comparison to standard EEG in newborns without severe hypoxic-ischemic encephalopathy who were at risk for seizures. METHODS The study included a consecutive series of 18 term newborns without severe hypoxic-ischemic encephalopathy, but with clinical signs suspicious of epileptic seizures, history of lo...

Pathological slowing in the electroencephalogram (EEG) is widely investigated for diagnosis of neurological disorders. Currently, gold standard detection visual inspection EEG by experts, which time-consuming and subjective. To address those issues, we propose three automated approaches to detect EEG: Threshold-based Detection System (TDS), Shallow Learning-based (SLDS), Deep (DLDS). These syst...

OBJECTIVE Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. APPROACH To improve EEG data quality substantially, we introduce methods that use a reusable referen...