Learning-dependent increases in sleep spindle density have been reported during nocturnal sleep immediately after the learning session. Here, we investigated experience-dependent changes in daytime sleep EEG activity after declarative learning of unrelated word pairs. At weekly intervals, 13 young male volunteers spent three 24 h sessions in the laboratory under carefully controlled homeostatic...

Brain-computer interface (BCI) technology is a means of communication that allows individuals with severe movement disability to communicate with external assistive devices using the electroencephalogram (EEG) or other brain signals. This paper presents an alternative neural information processing architecture using Schrödinger wave equation for enhancement of the raw EEG signal. The raw EEG si...

Universities, schools, and training centers are seeking to improve their computer-based [3] and distance learning classes through the addition of short training videos, often referred to as podcasts [4]. As distance learning and computer based training become more popular, it is of great interest to measure if students are attentive to recorded lessons and short training videos. The proposed re...

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...

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...

Electroencephalography (EEG) signals’ interpretation is based on waveform analysis, where meaningful information should emerge from a plethora of data. Nonetheless, the continuous increase in computational power and the development of new data processing algorithms in the recent years have put into reach the possibility of analysing raw EEG signals. Bearing that motivation, the authors propose ...

PURPOSE To describe the development and implementation of video EEG telemetry (VT) in the patient's home (home video telemetry, HVT) in a single centre. METHODS HVT met the UK Medical Research Council definition of a complex intervention, and we used its guidance to evaluate the process of piloting, evaluating, developing and implementing this new clinical service. The first phase was a feasi...

Recently, emotion classification from EEG data has attracted much attention with the rapid development of dry electrode techniques, machine learning algorithms, and various real-world applications of brain– computer interface for normal people. Until now, however, researchers had little understanding of the details of relationship between different emotional states and various EEG features. To ...

We hypothesized that control processes, as measured using electrophysiological (EEG) variables, influence the rate of learning of complex tasks. Specifically, we measured alpha power, event-related spectral perturbations (ERSPs), and event-related brain potentials during early training of the Space Fortress task, and correlated these measures with subsequent learning rate and performance in tra...