In presurgical monitoring, focal seizure onset is visually assessed from intracranial electroencephalogram (EEG), typically based on the selection of channels that show strongest changes in amplitude and frequency. As epileptic dynamics increasingly considered to reflect potentially distributed neural networks, it becomes important also assess interrelationships between channels. We propose a w...

The presence of cerebral palsy and that of slow growing brain tumors are risk factors for convulsive and nonconvulsive status epilepticus. Nonconvulsive status epilepticus (NCSE) needs electroencephalographic (EEG) monitoring to be confirmed as it may be clinically subtle. Furthermore, it may present with a variety of ictal EEG morphologies. We report a case of a patient with cerebral palsy and...

Electroencephalogram (EEG) reflects the brain activity and is widely used in biomedical research. However, analysis of this signal is still a challenging issue. This paper presents a hybrid approach for assessing stress using the EEG signal. It applies Multivariate Multi-scale Entropy Analysis (MMSE) for the data level fusion. Case-based reasoning is used for the classification tasks. Our preli...

Psychogenic nonepileptic seizures have long been known by many names. A short list includes hysteroepilepsy, hysterical seizures, pseudoseizures, nonepileptic events, nonepileptic spells, nonepileptic seizures, and psychogenic nonepileptic attacks. These events are typically misdiagnosed for years and are frequently treated as electrographic seizures and epilepsy. These patients experience all ...

Temporal lobectomy fails to control seizures in a considerable percentage of patients who do not have hippocampal sclerosis. One theoretical reason for failure of surgery is that some of these patients may in fact have extratemporal epilepsy. We present a 28-year-old woman with clinical and scalp electroencephalogram (EEG) evidence of right temporal lobe epilepsy (TLE) supported by functional i...

Automatic seizure detection is becoming popular in modern epilepsy monitoring units since it assists diagnostic monitoring and reduces manual review of large volumes of EEG recordings. In this paper, we describe the application of machine learning algorithms for building patient-specific seizure detectors on multiple frequency bands of intra-cranial electroencephalogram (iEEG) recorded by a den...

Automated monitoring of vigilance in attention intensive tasks such as air traffic control or sonar operation is highly desirable. As the operator monitors the instrument, the instrument would monitor the operator, insuring against lapses. We have taken a first step toward this goal by using feedforward neural networks trained with backpropagation to interpret event related potentials (ERPs) an...