Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might inc...

In this paper, unique approach is presented for the electroencephalography (EEG) signals analysis. This is based on Eigen values distribution of a matrix which is called as scaled Hankel matrix. This gives us a way to find out the number of Eigen values essential for noise reduction and extraction of signal in singular spectrum analysis. This paper gives us an approach to classify the EEG signa...

Electroencephalography (EEG) is the tool to record electrical activity over the scalp. This technique is widely used in clinical or research setting, since it is user friendly and non – invasive. In clinical setting, the EEG signal is used to diagnose the disease related to brain. In research setting, the usage of EEG signal is focused on rehabilitation; mental stress study . This paper present...

The electroencephalogram (EEG) is a complex signal and an important brain state indicator (e.g. waking, sleep, seizure). Modern brain research is intimately linked to the feasibility to record the EEG and to its quantitative analysis. EEG spectral analysis (decomposing a signal into its constituent frequency components) is an important method to investigate brain activity. Basic principals of s...

Brain waves are proposed as a biometric for verification of the identities of individuals in a small group. The approach is based on a novel two-stage biometric authentication method that minimizes both false accept error (FAE) and false reject error (FRE). These brain waves (or electroencephalogram (EEG) signals) are recorded while the user performs either one or several thought activities. As...

ASSESSMENT OF FETAL HEART RATE VARIABILITY AND REACTIVITY DURING LABOUR – A NOVEL APPROACH Sofia Blad 1 ENTROPY OF THE NEONATAL EEG Nils Löfgen 3 DETECTION OF BURSTS IN THE EEG OF POST ASPHYCTIC NEWBORNS Johan Löfhede 5 THE TRANSCEPHALIC ELECTRICAL IMPEDANCE METHOD PRINCIPLES FOR BRAIN TISSUE STATE MONITORING Fernando Seoane 7 SPECTRAL FEATURES OF THE EEG ARE UNLIKELY TO DIFFERENTIATE BETWEEN N...

We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures. This is a challenging problem because seizure manifestations on EEG are extremely variable both interand intra-patient. By simultaneously capturing spectral, temporal and spatial information our recurrent convolutional neural network learns a general spatially invari...

Using the EEG Motor Movement/Imagery database there is proposed an off-line analysis for a brain computer interface (BCI) paradigm. The purpose of the quantitative research is to compare classifier in order to determinate which of them has highest rates of classification. The power spectral density method is used to evaluate the (de)synchronizations that appear on Mu rhythm. The features extrac...

Electroencephalography (EEG) recordings during sleep are often contaminated by muscle and ocular artefacts, which can affect the results of spectral power analyses significantly. However, the extent to which these artefacts affect EEG spectral power across different sleep states has not been quantified explicitly. Consequently, the effectiveness of automated artefact-rejection algorithms in min...

background the importance of proper qualitative evaluation of eeg parameters during surgery has been recognized since many years. although none of the characteristics based on the frequency, entropy, and bi spectral characteristics have been regarded as a good predictor for detection of the depth of anesthesia alone. so it seems necessary to study multiple characteristics together. objectives i...