Generalized Deep Learning EEG Models for Cross-Participant and Cross-Task Detection of the Vigilance Decrement in Sustained Attention Tasks.

Tasks which require sustained attention over a lengthy period of time have been a focal point of cognitive fatigue research for decades, with these tasks including air traffic control, watchkeeping, baggage inspection, and many others. Recent research into physiological markers of mental fatigue indicate that markers exist which extend across all individuals and all types of vigilance tasks. This suggests that it would be possible to build an EEG model which detects these markers and the subsequent vigilance decrement in any task (i.

The Effects of Individual Differences, Non-Stationarity, and the Importance of Data Partitioning Decisions for Training and Testing of EEG Cross-Participant Models.

EEG-based deep learning models have trended toward models that are designed to perform classification on any individual (cross-participant models). However, because EEG varies across participants due to non-stationarity and individual differences, certain guidelines must be followed for partitioning data into training, validation, and testing sets, in order for cross-participant models to avoid overestimation of model accuracy. Despite this necessity, the majority of EEG-based cross-participant models have not adopted such guidelines.

Cross-Participant EEG-Based Assessment of Cognitive Workload Using Multi-Path Convolutional Recurrent Neural Networks.

Applying deep learning methods to electroencephalograph (EEG) data for cognitive state assessment has yielded improvements over previous modeling methods. However, research focused on cross-participant cognitive workload modeling using these techniques is underrepresented. We study the problem of cross-participant state estimation in a non-stimulus-locked task environment, where a trained model is used to make workload estimates on a new participant who is not represented in the training set.

A Randomized Controlled Trial of Core Strengthening Exercises in Helicopter Crewmembers with Low Back Pain.

Background: The purpose of this study was to determine if five core strengthening exercises would decrease pain severity and related disability in U.S. Air Force helicopter aircrew members with low back pain.

Effect of alertness level and backrest elevation on skin interface pressure.

Introduction: Critically ill patients may experience reduced mobility and sensation related to various pharmacologic therapies and treatments, making this patient population especially susceptible to pressure ulcers. An alert patient may be better able to reposition in response to discomfort, therefore preventing the development of pressure ulcers. However, little is known about the effect of an individual's alertness level on skin interface pressures.

Heart failure symptom clusters and functional status - a cross-sectional study.

Aim: To identify symptom clusters in individuals with heart failure and evaluate the relationship of the identified clusters to functional status.

Background: Heart Failure is a global health problem affecting approximately 1-2% of the adult population in developed countries worldwide. Individuals with heart failure may experience as many as nine symptoms and may limit activities that worsen their symptoms or adjust the way they engage in activities.

Development of an updated tensile neck injury criterion.

Background: Ejection neck safety remains a concern in military aviation with the growing use of helmet mounted displays (HMDs) worn for entire mission durations. The original USAF tensile neck injury criterion proposed by Carter et al. (4) is updated and an injury protection limit for tensile loading is presented to evaluate escape system and HMD safety.