Phase lag index and spectral power as QEEG features for identification of patients with mild cognitive impairment in Parkinson's disease.

Objectives: To identify quantitative EEG frequency and connectivity features (Phase Lag Index) characteristic of mild cognitive impairment (MCI) in Parkinson's disease (PD) patients and to investigate if these features correlate with cognitive measures of the patients.

Methods: We recorded EEG data for a group of PD patients with MCI (n = 27) and PD patients without cognitive impairment (n = 43) using a high-resolution recording system. The EEG files were processed and 66 frequency along with 330 connectivity (phase lag index, PLI) measures were calculated.

Quantitative EEG (QEEG) Measures Differentiate Parkinson's Disease (PD) Patients from Healthy Controls (HC).

To find out which Quantitative EEG (QEEG) parameters could best distinguish patients with Parkinson's disease (PD) with and without Mild Cognitive Impairment from healthy individuals and to find an optimal method for feature selection. Certain QEEG parameters have been seen to be associated with dementia in Parkinson's and Alzheimer's disease. Studies have also shown some parameters to be dependent on the stage of the disease.

Accelerated cognitive decline in a rodent model for temporal lobe epilepsy.

Objective: Cognitive impairment is frequently observed in patients with temporal lobe epilepsy. It is hypothesized that cumulative seizure exposure causes accelerated cognitive decline in patients with epilepsy. We investigated the influence of seizure frequency on cognitive decline in a rodent model for temporal lobe epilepsy.

Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection.

Continuous electroencephalographic monitoring of critically ill patients is an established procedure in intensive care units. Seizure detection algorithms, such as support vector machines (SVM), play a prominent role in this procedure. To correct for inter-human differences in EEG characteristics, as well as for intra-human EEG variability over time, dynamic EEG feature normalization is essential.

Optimal training dataset composition for SVM-based, age-independent, automated epileptic seizure detection.

Automated seizure detection is a valuable asset to health professionals, which makes adequate treatment possible in order to minimize brain damage. Most research focuses on two separate aspects of automated seizure detection: EEG feature computation and classification methods. Little research has been published regarding optimal training dataset composition for patient-independent seizure detection.