The motion generated at the capturing time of electro-encephalography (EEG) signal leads to the artifacts, which may reduce the quality of obtained information. Existing artifact removal methods use canonical correlation analysis (CCA) for removing artifacts along with ensemble empirical mode decomposition (EEMD) and wavelet transform (WT). A new approach is proposed to further analyse and impr...

me spe yn pro e cc tra sin beh sep e It two-dimensional signals can be reconstructed, with a axima of their Electro encephalography (EEG) is an important ical tool in theoretical study, diagnosis and tment of several neurological disorders such as lepsy and sleeping disorders. For the most part, an epilepsy is the intrinsic brain pathology. Its jor manifestation in the epileptic seizure, which ...

Case report The patient, Mrs J.S., aged 54, was first admitted to Stobhill General Hospital on 17 August 1965. Five years previously she had been investigated elsewhere because of haematuria and hypertension. A diagnosis of chronic pyelonephritis affecting the left kidney was made on the evidence of the intravenous pyelogram. She had been taking methyldopa, 500 mg t.i.d. since that time. Six we...

The brain activity of humans with tinnitus of various etiologies is typically studied with electro- and magneto-encephalography and functional magnetic resonance imaging-based imaging techniques. Consequently, they measure population responses and mostly from the neocortex. The latter also underlies changes in neural networks that may be attributed to tinnitus. However, factors not strictly rel...

The need to infer brain states in a data driven approach is crucial for BCI applications as well as for neuroscience research. In this work we present a novel classification framework based on Regularized Linear Regression classifier constructed from time-frequency decomposition of an EEG (electro-encephalography) signal. The regression is then used to derive a model of frequency distributions ...

This paper addresses the structurally-constrained sparse decomposition of multi-dimensional signals onto overcomplete families of vectors, called dictionaries. The contribution of the paper is threefold. Firstly, a generic spatio-temporal regularization term is designed and used together with the standard 1 regular-ization term to enforce a sparse decomposition preserving the spatio-temporal st...

BACKGROUND Transcranial Doppler Ultrasound (TCD) is a sensitive, real time tool for monitoring cerebral blood flow velocity (CBFV). This technique is fast, accurate, reproducible and noninvasive. In the setting of congenital heart surgery, TCD finds application in the evaluation of cerebral blood flow variations during cardiopulmonary bypass (CPB). METHODOLOGY We performed a search on human s...

PURPOSE There is a need for a seizure-detection system that can be used long-term and in home situations for early intervention and prevention of seizure related side effects including SUDEP (sudden unexpected death in epileptic patients). The gold standard for monitoring epileptic seizures involves video/EEG (electro-encephalography), which is uncomfortable for the patient, as EEG electrodes a...

Temporal seizures due to hippocampal origins are very common among epileptic patients. Presented is a novel seizure prediction approach employing correlation and chaos theories. The early identification of seizure signature allows for various preventive measures to be undertaken. Electro-encephalography signals are spectrally broken down into the following sub-bands: delta; theta; alpha; beta; ...