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...

Electroencephalogram (EEG) signals reveal electrical activity of brain in a person. Brain cells interact by impulses even during sleep. Any disruptions to these induce problems the individual. Hence clinicians analyze EEG readings comprehend impulses. and its sub bands depict pattern human brain. data comprises transient components, spikes, other sorts artifacts due eye blinking, movement indiv...

Attentive learning is an important feature of the process. It provides a beneficial experience and plays key role in generating positive outcomes. Most studies widely applied electroencephalogram (EEG) to measure human attention level. Although most use EEG handcrafted features statistical methods classify level, more effective technique still needed. In this paper, we aim analyze participants’...

The high temporal resolution and the asymmetric spatial activations are essential attributes of electroencephalogram (EEG) underlying emotional processes in brain. To learn dynamics asymmetry EEG towards accurate generalized emotion recognition, we propose TSception, a multi-scale convolutional neural network that can classify emotions from EEG. TSception consists dynamic temporal, spatial, hig...

Learning is assumed to induce specific changes in neuronal activity during sleep that serve the consolidation of newly acquired memories. To specify such changes, we measured electroencephalographic (EEG) coherence during performance on a declarative learning task (word pair associations) and subsequent sleep. Compared with a nonlearning control condition, learning performance was accompanied w...

Electroencephalogram (EEG) signal is a modest measure of electric flow in a human brain. It is responsible for information flow through the neurons in the brain which controls and monitors the full torso. Hence, to widening and in-depth understanding of effectiveness in EEG signal analysis is the primary focus of this paper. Moreover, machine learning techniques often proven as more efficacious...

The ability to form relational categories for objects that share few features in common is a hallmark of human cognition. However until recently, neuroimaging research largely focused solely on how people acquire categories defined by features. In the current electroencephalography (EEG) study, we examine how relational and feature-based category learning compare in well-matched learning tasks....

Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit novel features from the collective dynamics of the system for classification. Herein, we also propose a new framework for learning optimal filter...

Reinforcement learning requires the dynamic interplay of several specialized networks distributed across the brain. A potential mechanism to establish accurate temporal coordination among these paths is through the synchronization of neuronal activity to a common rhythm of neuronal firing. Previous EEG studies have suggested that theta oscillatory activity might be crucial in the integration of...