Biomarkers.

Background: Quantitative EEG measures can be used as biosignatures of disease conditions. As such, the effect of interventions/treatments can be studied by longitudinal analysis of changes in these measures. The consistency of these measures can be assessed by test-retest reliability scores such as intra-class correlation coefficient (ICC) that depends on intra- and inter-subject variability.

Biomarkers.

Background: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia, but differentiating between them can be challenging due to overlapping symptoms [1]. Quantitative electroencephalography (EEG) is emerging as a promising tool to identify potential biosignatures that can distinguish AD and FTD [2]-[5]. Prior EEG research has revealed slowing of the posterior dominant rhythm (PDR) in both AD and FTD patients compared to controls, reflecting underlying neurodegeneration.

EEG and ERP biosignatures of mild cognitive impairment for longitudinal monitoring of early cognitive decline in Alzheimer's disease.

Cognitive decline in Alzheimer's disease is associated with electroencephalographic (EEG) biosignatures even at early stages of mild cognitive impairment (MCI). The aim of this work is to provide a unified measure of cognitive decline by aggregating biosignatures from multiple EEG modalities and to evaluate repeatability of the composite measure at an individual level. These modalities included resting state EEG (eyes-closed) and two event-related potential (ERP) tasks on visual memory and attention.

Alterations in Electroencephalography Theta as Candidate Biomarkers of Acute Cannabis Intoxication.

The trend toward cannabis legalization in the United States over the past two decades has unsurprisingly been accompanied by an increase in the number of cannabis users and use patterns that potentially pose wider risks to the public like driving under the influence. As such, it is becoming increasingly important to develop methods to accurately quantify cannabis intoxication and its associated impairments on cognitive and motor function. Electroencephalography (EEG) offers a non-invasive method for quantitatively assessing neurophysiological biomarkers of intoxication and impairment with a high degree of temporal resolution.

EEG event related potentials in sustained, focused and divided attention tasks: Potential biomarkers for cognitive impairment in HIV patients.

Objectives: The objective of this study was to assess the usability of event-related-potentials (ERPs) during sustained, focused, and divided attention tasks as biomarkers for cognitive decline in HIV patients.

Methods: EEG was acquired using a mobile/wireless 9-channel system in 39 persons with HIV, with well-controlled immune function and 63 healthy control participants (HCs) during three ERP tasks: sustained attention, focused attention, and divided attention.

Results: The HIV-group evidenced smaller late positive potential (LPP) and larger P200 amplitudes across the tasks compared to the HC group.

Resting state EEG biomarkers of cognitive decline associated with Alzheimer's disease and mild cognitive impairment.

In this paper, we explore the utility of resting-state EEG measures as potential biomarkers for the detection and assessment of cognitive decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Neurophysiological biomarkers of AD derived from EEG and FDG-PET, once characterized and validated, would expand the set of existing diagnostic molecular biomarkers of AD pathology with associated biomarkers of disease progression and neural dysfunction. Since symptoms of AD often begin to appear later in life, successful identification of EEG-based biomarkers must account for age-related neurophysiological changes that occur even in healthy individuals.

EEG signatures of contextual influences on visual search with real scenes.

The use of scene context is a powerful way by which biological organisms guide and facilitate visual search. Although many studies have shown enhancements of target-related electroencephalographic activity (EEG) with synthetic cues, there have been fewer studies demonstrating such enhancements during search with scene context and objects in real world scenes. Here, observers covertly searched for a target in images of real scenes while we used EEG to measure the steady state visual evoked response to objects flickering at different frequencies.

Transcranial Impedance Changes during Sleep: A Rheoencephalography Study.

Objective: To demonstrate the utility of rheoencephalography (REG) for measuring cerebral blood flow and fluid dynamics during different stages of sleep.

Methods: Anteroposterior cranial electrical impedance was measured with concurrent polysomnography in a group of healthy subjects during sleep. Transcranial electrical impedance was characterized by measuring the peak-to-trough and envelope of the filtered pulsative REG signal as well as its frequency.

EEG-Based Neurocognitive Metrics May Predict Simulated and On-Road Driving Performance in Older Drivers.

The number of older drivers is steadily increasing, and advancing age is associated with a high rate of automobile crashes and fatalities. This can be attributed to a combination of factors including decline in sensory, motor, and cognitive functions due to natural aging or neurodegenerative diseases such as HIV-Associated Neurocognitive Disorder (HAND). Current clinical assessment methods only modestly predict impaired driving.

Interactive exploration of surveillance video through action shot summarization and trajectory visualization.

We propose a novel video visual analytics system for interactive exploration of surveillance video data. Our approach consists of providing analysts with various views of information related to moving objects in a video. To do this we first extract each object's movement path.

A method for detecting nonlinear determinism in normal and epileptic brain EEG signals.

A robust method of detecting determinism for short time series is proposed and applied to both healthy and epileptic EEG signals. The method provides a robust measure of determinism through characterizing the trajectories of the signal components which are obtained through singular value decomposition. Robustness of the method is shown by calculating proposed index of determinism at different levels of white and colored noise added to a simulated chaotic signal.

Detecting determinism in EEG signals using principal component analysis and surrogate data testing.

A novel method is proposed here to determine whether a time series is deterministic even in the presence of noise. The method is the extension of an existing method based on smoothness analysis of the signal in state space with surrogate data testing. While classical measures fail to detect determinism when the time series is corrupted by noise, the proposed method can clearly distinguish between pure stochastic and originally deterministic but noisy time series.