The analysis of electroencephalographic signals in frequency is usually not performed by transforms that can extract the instantaneous characteristics of the signal. However, the non-steady state nature of these low voltage electrical signals makes them suitable for this kind of analysis. In this paper a novel tool based on Stockwell transform is tested, and compared with techniques such as Hil...

We have previously shown that classification of single-trial electroencephalographic (EEG) recordings is improved by the use of either a niultichannel classifier or the best independent component over a single channel classifier. In this paper, we introduce a classifier that makes explicit use of multiple independent components. Two models are compared. The first ("direct") model uses independe...

This work proposes a new technique for the automatic detection of electroencephalographic (EEG) arousals in sleep polysomnographic recordings. We have developed a non-computationally complex algorithm with the idea of providing an easy integration into different software platforms. The approach combines different well-known signal analyses to identify relevant arousal patterns. Special emphasis...

Analyses of electroencephalographic signals and subsequent diagnoses can only be done effectively on long term recordings that preserve the signals’ morphologies. Currently, electroencephalographic signals are obtained at Nyquist rate or higher, thus introducing redundancies. Existing compression methods remove these redundancies, thereby achieving compression. We propose an alternative compres...

Serial electroencephalographic recordings were made in 70 diabetic children and findings were related to age at electroencephalography and at diagnosis, duration of diabetes, daily insulin dose, long term metabolic control assessed by glycated haemoglobin A1 (HbA1) concentrations, and severe hypoglycaemic episodes. Abnormalities were found in 18 (26%) of diabetic children, and in only five (7%)...

OBJECTIVES To assess the effect of continuous electroencephalographic monitoring on the decision to treat seizures in the inpatient setting, particularly in the intensive care unit. DESIGN Retrospective cohort study. SETTING Medical and neuroscience intensive care units and neurological wards. PATIENTS Three hundred consecutive nonelective continuous electroencephalographic monitoring stu...

Background. The term “postcovid syndrome” is firmly entrenched in medical terminology, but many aspects of its clinical manifestations are not well understood. Aim. To establish the presence nature and severity changes bioelectrical activity brain COVID-19 survivors, as their relationship with formed neurological neuropsychological syndromes during convalescence. Materials methods. A dynamic st...

OBJECTIVE To study the significance of electroencephalographic background activity and positive sharp waves in neonatal electroencephalogram as prognostic of cerebral palsy. METHOD We studied prospectively and sequentially 73 newborns who had severe neonatal complications (neonatal anoxia, seizures, respiratory distress, sepsis, and meningitis). Nineteen newborns were excluded and 54 children...

In recent years, brain-computer interface (BCI) technology has emerged very rapidly. Brain-computer interfaces (BCIs) bring us a new communication interface technology which can translate brain activities into control signals of devices like computers, robots. The preprocessing of electroencephalographic (EEG) signal and translation algorithms play an important role in EEG-based BCIs. In this s...

EEGLAB (sccn.ucsc.edu/eeglab) is an easily extensible, highly evolved, and now widely used open source environment for signal processing of electroencephalographic data running on MATLAB (The Mathworks, Inc.). Here we introduce MEEG, an EEGLAB plug-in that appears in the EEGLAB menu of users who download it. MEEG gathers functions from EEGLAB and other MATLAB-based open source frameworks to rea...