Comparison of a Novel Dry Electrode Headset to Standard Routine EEG in Veterans.

Objective: This purpose of this study was to evaluate the usefulness of a prototype battery-powered dry electrode system (DES) EEG recording headset in Veteran patients by comparing it with standard EEG.

Methods: Twenty-one Veterans had both a standard electrode system recording and DES recording in nine different patient states at the same encounter. Setup time, patient comfort, and subject preference were measured.

A study of morphology-based wavelet features and multiple-wavelet strategy for EEG signal classification: results and selected statistical analysis.

Automatic detection and classification of Epileptiform transients is an open and important clinical issue. In this paper, we test 5 feature sets derived from a group of morphology-based wavelet features and compare the results with that of a Guler-suggested feature set. We also implement a multiple-mother-wavelet strategy and compare performance with the usual single-mother-wavelet strategy.

Morphology-based wavelet features and multiple mother wavelet strategy for spike classification in EEG signals.

New wavelet-derived features and strategies that can improve autonomous EEG classifier performance are presented. Various feature sets based on the morphological structure of wavelet subband coefficients are derived and evaluated. The performance of these new feature sets is superior to Guler's classic features in both sensitivity and specificity.

Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis.

The routine scalp electroencephalogram (rsEEG) is the most common clinical neurophysiology procedure. The most important role of rsEEG is to detect evidence of epilepsy, in the form of epileptiform transients (ETs), also known as spike or sharp wave discharges. Due to the wide variety of morphologies of ETs and their similarity to artifacts and waves that are part of the normal background activity, the task of ET detection is difficult and mistakes are frequently made.