Improving Eye Motion Sequence Recognition Using Electrooculography Based on Context-Dependent HMM.

Eye motion-based human-machine interfaces are used to provide a means of communication for those who can move nothing but their eyes because of injury or disease. To detect eye motions, electrooculography (EOG) is used. For efficient communication, the input speed is critical.

Detection of EEG-resting state independent networks by eLORETA-ICA method.

Recent functional magnetic resonance imaging (fMRI) studies have shown that functional networks can be extracted even from resting state data, the so called "Resting State independent Networks" (RS-independent-Ns) by applying independent component analysis (ICA). However, compared to fMRI, electroencephalography (EEG) and magnetoencephalography (MEG) have much higher temporal resolution and provide a direct estimation of cortical activity. To date, MEG studies have applied ICA for separate frequency bands only, disregarding cross-frequency couplings.

EEG and Neuronal Activity Topography analysis can predict effectiveness of shunt operation in idiopathic normal pressure hydrocephalus patients.

Idiopathic normal pressure hydrocephalus (iNPH) is a neuropsychiatric syndrome characterized by gait disturbance, cognitive impairment and urinary incontinence that affect elderly individuals. These symptoms can potentially be reversed by cerebrospinal fluid (CSF) drainage or shunt operation. Prior to shunt operation, drainage of a small amount of CSF or "CSF tapping" is usually performed to ascertain the effect of the operation.

Diagnosis method of mild cognitive impairment based on power variance of EEG.

Mild cognitive impairment (MCI) patients and healthy people were classified by using a "power variance function (PVF)", namely, an index of electroencephalography (EEG) proposed in a previous report. PVF is defined by calculating variance of the power variability of an EEG signal at each frequency of the signal using wavelet transform. After confirming that the distribution of PVFs of the subjects was a normal distribution at each frequency, the distributions of PVFs of 25 MCI patients and those of 57 healthy people were compared in terms of Z-score.

Normalized power variance change between pre-ictal and ictal phase of an epilepsy patient using NAT analysis: a case study.

Variance of state variables shifts due to phase-instability and may serve as an early-warning signal of phase transition of complex systems such as an epileptic seizure of brain cortical activity. Neuronal Activity Topology (NAT) analysis calculates a normalized-power-variance (NPV) of electroencephalogram (EEG) data in each frequency band to obtain relative values comparable among different power states.

The relationship between the diagnosis method of neuronal dysfunction (DIMENSION) and brain pathology in the early stages of Alzheimer's disease.

Objectives: To examine whether the diagnosis method of neuronal dysfunction (DIMENSION), a new electroencephalogram (EEG) analysis method, reflected pathological changes in the early stages of Alzheimer's disease (AD), we conducted a comparative study of cerebrospinal fluid markers and single-photon emission computed tomography.

Methods: Subjects cincluded 32 patients in the early stages of AD with a Mini-Mental State Examination score ≥24 (14 men, 18 women; mean age, 77.3 ± 9.

EEG markers for characterizing anomalous activities of cerebral neurons in NAT (neuronal activity topography) method.

A pair of markers, sNAT and vNAT, is derived from the electroencephalogram (EEG) power spectra (PS) recorded for 5 min with 21 electrodes (4-20 Hz) arranged according to the 10-20 standard. These markers form a new diagnosis tool "NAT" aiming at characterizing various brain disorders. Each signal sequence is divided into segments of 0.

Audio representations of multi-channel EEG: a new tool for diagnosis of brain disorders.

Objective: The objective of this paper is to develop audio representations of electroencephalographic (EEG) multichannel signals, useful for medical practitioners and neuroscientists. The fundamental question explored in this paper is whether clinically valuable information contained in the EEG, not available from the conventional graphical EEG representation, might become apparent through audio representations.

Methods And Materials: Music scores are generated from sparse time-frequency maps of EEG signals.

Quantifying statistical interdependence, part III: N > 2 point processes.

Stochastic event synchrony (SES) is a recently proposed family of similarity measures. First, "events" are extracted from the given signals; next, one tries to align events across the different time series. The better the alignment, the more similar the N time series are considered to be.

Improving the specificity of EEG for diagnosing Alzheimer's disease.

Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice.

Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin?

Medical studies have shown that EEG of Alzheimer's disease (AD) patients is "slower" (i.e., contains more low-frequency power) and is less complex compared to age-matched healthy subjects.

Research of the characteristics of Alzheimer's disease using EEG.

In this paper, we propose a new method for diagnosing Alzheimer's disease (AD) on the basis of electroencephalograms (EEG). The method, which is termed "Power Variance Function (PVF) method", indicates the variance of the power at each frequency. By using the proposed method, the power of EEG at each frequency was calculated using Wavelet transform, and the corresponding variances were defined as PVF.

Fronto-parietal coupling of brain rhythms in mild cognitive impairment: a multicentric EEG study.

Electroencephalographic (EEG) data were recorded in 69 normal elderly (Nold), 88 mild cognitive impairment (MCI), and 109 mild Alzheimer's disease (AD) subjects at rest condition, to test whether the fronto-parietal coupling of EEG rhythms is in line with the hypothesis that MCI can be considered as a pre-clinical stage of the disease at group level. Functional coupling was estimated by synchronization likelihood of Laplacian-transformed EEG data at electrode pairs, which accounts for linear and non-linear components of that coupling. Cortical rhythms of interest were delta (2-4Hz), theta (4-8Hz), alpha 1 (8-10.

EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease.

Objective: Development of an EEG preprocessing technique for improvement of detection of Alzheimer's disease (AD). The technique is based on filtering of EEG data using blind source separation (BSS) and projection of components which are possibly sensitive to cortical neuronal impairment found in early stages of AD.

Methods: Artifact-free 20s intervals of raw resting EEG recordings from 22 patients with Mild Cognitive Impairment (MCI) who later proceeded to AD and 38 age-matched normal controls were decomposed into spatio-temporally decorrelated components using BSS algorithm 'AMUSE'.

A new EEG method for estimating cortical neuronal impairment that is sensitive to early stage Alzheimer's disease.

Objectives: To test the hypothesis that elecetroencephalographic (EEG) analysis is sensitive to cortical neuronal impairment in early stage Alzheimer's disease (AD), and that this analysis correlates with corresponding changes in cerebral blood flow.

Methods: We examined an EEG measure of neuronal impairment in the cerebral cortex in terms of its ability to detect very mild AD. This measure, the mean value of the resting state EEG alpha dipolarity (D(alpha)), approaches unity without cortical sulcal lesions, whereas brains with randomly distributed cortical sulcal lesions lower D(alpha) values well below unity.