Characterizing Population EEG Dynamics throughout Adulthood.

For decades, electroencephalography (EEG) has been a useful tool for investigating the neural mechanisms underlying human psychological processes. However, the amount of time needed to gather EEG data means that most laboratory studies use relatively small sample sizes. Using the Muse, a portable and wireless four-channel EEG headband, we obtained EEG recordings from 6029 subjects 18-88 years in age while they completed a category exemplar task followed by a meditation exercise.

Attentional and affective consequences of technology supported mindfulness training: a randomised, active control, efficacy trial.

Background: Mindfulness training (MT) programs represent an approach to attention training with well-validated mental health benefits. However, research supporting MT efficacy is based predominantly on weekly-meeting, facilitator-led, group-intervention formats. It is unknown whether participants might benefit from neurofeedback-assisted, technology-supported MT (N-tsMT), in which meditation is delivered individually, without the need for a facilitator, travel to a training site, or the presence of a supportive group environment.

Comparison of an Electronic and Paper-based Montreal Cognitive Assessment Tool.

This pilot study compared a novel electronic Montreal Cognitive Assessment (eMoCA) tool to the original paper-based MoCA. Potential participants were approached at primary care practices, a geriatric day hospital, and a university campus. Each of the 401 participants were randomly assigned to either the eMoCA (N=182) or MoCA (N=219).

Probabilistic muscle characterization using quantitative electromyography: application to facioscapulohumeral muscular dystrophy.

Based on quantitative electromyography, a muscle can be categorized as normal or affected by a neuromuscular disorder. The objective of this work was to compare the utility of probabilistic to conventional means and outlier methods of categorization of myopathic and normal muscles. Various sets of motor unit potential (MUP) features detected in biceps brachii muscles of control subjects and patients with facioscapulohumeral muscular dystrophy were used to categorize them as normal or myopathic based on conventional means and outlier categorization (CMC) as well as a new probabilistic muscle categorization (PMC).

Using motor unit potential characterizations to estimate neuromuscular disorder level of involvement.

Based on the analysis of electromyographic (EMG) data muscles are often characterized as normal or affected by a neuromuscular disorder. Motor unit potential (MUP) characterizations comprised of the conditional probabilities of a MUP being detected from a muscle of each of the following categories: myopathic, normal, and neuropathic, were estimated. The sets of MUP characterizations of a set of MUPs detected in a muscle were averaged to produce a set of muscle characterization measures related to the probability of the muscle belonging to each category conditioned on the set of MUPs detected.