System and Method for Providing Model-Based Alerting of Spatial Disorientation to a Pilot

A system and method monitor aircraft state parameters, for example, aircraft movement and flight parameters, applies those inputs to a spatial disorientation model, and makes a prediction of when pilot may become spatially disoriented. Once the system predicts a potentially disoriented pilot, the sensitivity for alerting the pilot to conditions exceeding a threshold can be increased and allow for an earlier alert to mitigate the possibility of an incorrect control input

Trial-by-Trial Variations in Subjective Attentional State are Reflected in Ongoing Prestimulus EEG Alpha Oscillations

Parieto-occipital electroencephalogram (EEG) alpha power and subjective reports of attentional state are both associated with visual attention and awareness, but little is currently known about the relationship between these two measures. Here, we bring together these two literatures to explore the relationship between alpha activity and participants’ introspective judgments of attentional state as each varied from trial-to-trial during performance of a visual detection task. We collected participants’ subjective ratings of perceptual decision confidence and attentional state on continuous scales on each trial of a rapid serial visual presentation detection task while recording EEG. We found that confidence and attentional state ratings were largely uncorrelated with each other, but both were strongly associated with task performance and post-stimulus decision-related EEG activity. Crucially, attentional state ratings were also negatively associated with prestimulus EEG alpha power. Attesting to the robustness of this association, we were able to classify attentional state ratings via prestimulus alpha power on a single-trial basis. Moreover, when we repeated these analyses after smoothing the time series of attentional state ratings and alpha power with increasingly large sliding windows, both the correlations and classification performance improved considerably, with the peaks occurring at a sliding window size of approximately 7 min worth of trials. Our results therefore suggest that slow fluctuations in attentional state in the order of minutes are reflected in spontaneous alpha power. Since these subjective attentional state ratings were associated with objective measures of both behavior and neural activity, we suggest that they provide a simple and effective estimate of task engagement that could prove useful in operational settings that require human operators to maintain a sustained focus of visual attention

Rapid serial visual presentation triage prioritization based on user state assessment

A method and system are provided that prioritize the output of an image triage that is based on rapid serial visual presentation. User responses and estimates of the effectiveness with which each image is likely to have been processed by a user are employed for post triage image prioritization of potential targets. Images associated with a user response, processed during optimal user states, are assigned the highest priority for post triage examination, as targets are likely. Images without a user response that are processed during optimal user states are assigned the lowest priority, as these are unlikely to contain targets. Images with a user response that are processed during suboptimal states are assigned a medium priority, as these are likely to contain a high number of false positives. Images without a user response, processed during suboptimal user states are flagged for reprocessing as these may contain targets that the user may not have detected

Channel Selection and Feature Projection for Cognitive Load Estimation Using Ambulatory EEG

We present an ambulatory cognitive state classification system to assess the subject's mental load based on EEG measurements. The ambulatory cognitive state estimator is utilized in the context of a real-time augmented cognition (AugCog) system that aims to enhance the cognitive performance of a human user through computer-mediated assistance based on assessments of cognitive states using physiological signals including, but not limited to, EEG. This paper focuses particularly on the offline channel selection and feature projection phases of the design and aims to present mutual-information-based techniques that use a simple sample estimator for this quantity. Analyses conducted on data collected from 3 subjects performing 2 tasks (n-back/Larson) at 2 difficulty levels (low/high) demonstrate that the proposed mutual-information-based dimensionality reduction scheme can achieve up to 94% cognitive load estimation accuracy

A hybrid generative/discriminative method for EEG evoked potential detection

I. INTRODUCTION Generative and discriminative learning approaches are two prevailing and powerful, yet different, paradigms in machine leaning. Generative learning models, such as Bayesian inference [1] attempt to model the underlying distributions of the variables in order to compute classification and regression functions. These methods provide a rich framework for learning from prior knowledge. Discriminative learning models, such as support vector machines (SVM) [2] avoid generative modeling by directly optimizing a mapping from the inputs to the desired outputs by adjusting the resulting classification boundary. These latter methods commonly demonstrate superior performance in classification. Recently, researchers have investigated the relationship between these two learning paradigms and have attempted to combine their complementary strength

Blinding efficacy and adverse events following repeated transcranial alternating current, direct current, and random noise stimulation.

As transcranial electrical stimulation (tES) protocols advance, assumptions underlying the technique need to be retested to ensure they still hold. Whilst the safety of stimulation has been demonstrated mainly for a small number of sessions, and small sample size, adverse events (AEs) following multiple sessions remain largely untested. Similarly, whilst blinding procedures are typically assumed to be effective, the effect of multiple stimulation sessions on the efficacy of blinding procedures also remains under question. This is especially relevant in multisite projects where small unintentional variations in protocol could lead to inter-site difference. We report AE and blinding data from 1,019 participants who received up to 11 semi-consecutive sessions of active or sham transcranial alternating current stimulation (tACS), direct current stimulation (tDCS), and random noise stimulation (tRNS), at 4 sites in the UK and US. We found that AEs were often best predicted by factors other than tES, such as testing site or session number. Results from the blinding analysis suggested that blinding was less effective for tDCS and tACS than tRNS. The occurrence of AEs did not appear to be linked to tES despite the use of smaller electrodes or repeated delivery. However, blinding efficacy was impacted in tES conditions with higher cutaneous sensation, highlighting a need for alternative stimulation blinding protocols. This may be increasingly necessary in studies wishing to deliver stimulation with higher intensities

FAST-Phase1: Flexible Adaptive and Synergistic Training (Pre-Registration)

The primary objective of the Flexible Adaptive and Synergistic Training (FAST) effort is to develop and validate interventions designed to enhance reasoning and problem solving skills. The interventions of interest include cognitive training, focusing on core processes associated with executive function (EF), and low-current transcranial electrical stimulation (tES) to enhance the activity of the cortical substrate that is recruited by the EF training tasks. The FAST effort aims to validate these interventions in the context of a study involving 440 individuals. This research is supported by the Intelligence Advanced Research Projects Agency (IARPA) via contract# 2014-13121700007 isssued to Honeywell International. Members of the SHARP team include researchers representing Honeywell Labs, Harvard Medical School, Northeastern University, Oxford University, and SimCoach Games. The points noted here reflect the position of the research team and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, the Office of the Director of National Intelligence, or the U.S. Government