Adaptive Filtering with Fitted Noise Estimate (AFFiNE): Blink Artifact Correction in Simulated and Real P300 Data.

(1) Background: The electroencephalogram (EEG) is frequently corrupted by ocular artifacts such as saccades and blinks. Methods for correcting these artifacts include independent component analysis (ICA) and recursive-least-squares (RLS) adaptive filtering (-AF). Here, we introduce a new method, AFFiNE, that applies Bayesian adaptive regression spline (BARS) fitting to the adaptive filter's reference noise input to address the known limitations of both ICA and RLS-AF, and then compare the performance of all three methods.

Predicting and mitigating fatigue effects due to sleep deprivation: A review.

The deleterious effects of insufficient sleep have been well-established in the literature and can lead to a wide range of adverse health outcomes. Some of the most replicated findings demonstrate significant declines in cognitive functions such as vigilance and executive attention, psychomotor and cognitive speed, and working memory. Consequently, these decrements often lead individuals who are in a fatigued state to engage in substandard performance on everyday tasks.

Effects of Neuronic Shutter Observed in the EEG Alpha Rhythm.

The posterior alpha (α) rhythm, seen in human electroencephalogram (EEG), is posited to originate from cycling inhibitory/excitatory states of visual relay cells in the thalamus. These cycling states are thought to lead to oscillating visual sensitivity levels termed the "neuronic shutter effect." If true, perceptual performance should be predictable by observed α phase (of cycling inhibitory/excitatory states) relative to the timeline of afferentiation onto the visual cortex.

Spectral Estimation Methods for Evaluating iPPG Pulse Rate Variability.

Non-contact measurement of physiological parameters, like pulse rate variability (PRV), has numerous applications in medicine and affective computing. PRV is an informative measure of autonomic nervous system activity. Spectral estimation from unevenly sampled, non-stationary data is integral to pulse rate variability frequency-domain analysis.

Learning From the Slips of Others: Neural Correlates of Trust in Automated Agents.

With the rise of increasingly complex artificial intelligence (AI), there is a need to design new methods to monitor AI in a transparent, human-aware manner. Decades of research have demonstrated that people, who are not aware of the exact performance levels of automated algorithms, often experience a mismatch in expectations. Consequently, they will often provide either too little or too much trust in an algorithm.

Fusing Partial Camera Signals for Noncontact Pulse Rate Variability Measurement.

Remote camera-based measurement of physiology has great potential for healthcare and affective computing. Recent advances in computer vision and signal processing have enabled photoplethysmography (PPG) measurement using commercially available cameras. However, there remain challenges in recovering accurate noncontact PPG measurements in the presence of rigid head motion.

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.

Measuring pulse rate variability using long-range, non-contact imaging photoplethysmography.

Camera-based measurement of the blood volume pulse via non-contact, imaging photoplethysmography is a very popular approach for measuring pulse rate using a remote imaging sensor. Comparatively less attention has been paid to the usefulness of the method for measuring features of pulse rate variability, and even less focus has been put on the accuracy of any cardiac activity feature that can be achieved at long imager-to-subject distances. In this study, video was recorded from 19 participants, while at rest, at a distance of 25 meters from the imaging sensor.

Day-to-day variability in hybrid, passive brain-computer interfaces: comparing two studies assessing cognitive workload.

As hybrid, passive brain-computer interface systems become more advanced, it is important to grow our understanding of how to produce generalizable pattern classifiers of physiological data. One of the most difficult problems in applying machine learning algorithms to these data types is nonstationarity, which can evolve over the course of hours and days, and is more susceptible to changes resulting from complex cognitive function in comparison to simple, stimulus-based processes. This nonstationarity, referenced as day-to-day variability, results in the inability of many learning algorithms to generalize to new data.

A survey of remote optical photoplethysmographic imaging methods.

In recent years researchers have presented a number of new methods for recovering physiological parameters using just low-cost digital cameras and image processing. The ubiquity of digital cameras presents the possibility for many new, low-cost applications of vital sign monitoring. In this paper we present a review of the work on remote photoplethysmographic (PPG) imaging using digital cameras.

Electrode replacement does not affect classification accuracy in dual-session use of a passive brain-computer interface for assessing cognitive workload.

The passive brain-computer interface (pBCI) framework has been shown to be a very promising construct for assessing cognitive and affective state in both individuals and teams. There is a growing body of work that focuses on solving the challenges of transitioning pBCI systems from the research laboratory environment to practical, everyday use. An interesting issue is what impact methodological variability may have on the ability to reliably identify (neuro)physiological patterns that are useful for state assessment.

Lack of association between human plasma oxytocin and interpersonal trust in a Prisoner's Dilemma paradigm.

Expanding interest in oxytocin, particularly the role of endogenous oxytocin in human social behavior, has created a pressing need for replication of results and verification of assay methods. In this study, we sought to replicate and extend previous results correlating plasma oxytocin with trust and trustworthy behavior. As a necessary first step, the two most commonly used commercial assays were compared in human plasma via the addition of a known quantity of exogenous oxytocin, with and without sample extraction.

Frontal midline θ power as an index of listening effort.

Attempts to identify physiological correlates of listening effort have mainly focused on peripheral measures (e.g. pupillometry) and auditory-evoked/event-related potentials.

Coadaptive aiding and automation enhance operator performance.

Objective: In this work, we expand on the theory of adaptive aiding by measuring the effectiveness of coadaptive aiding, wherein we explicitly allow for both system and user to adapt to each other.

Background: Adaptive aiding driven by psychophysiological monitoring has been demonstrated to be a highly effective means of controlling task allocation and system functioning. Psychophysiological monitoring is uniquely well suited for coadaptation, as malleable brain activity may be used as a continuous input to the adaptive system.

A practical method for quickly determining electrode positions in high-density EEG studies.

This report describes a simple and practical method for determining electrode positions in high-density EEG studies. This method reduces the number of electrodes for which accurate three-dimensional location must be measured, thus minimizing experimental set-up time and the possibility of digitization error. For each electrode cap, a reference data set is first established by placing the cap on a reference head and digitizing the 3-D position of each channel.

An assessment of non-stationarity in physiological cognitive state assessment using artificial neural networks.

With increased attention toward physiological cognitive state assessment as a component in the larger field of applied neuroscience, the need to develop methods for robust, stable assessment of cognitive state has been expressed as critical to designing effective augmented human-machine systems. The technique of cognitive state assessment, as well as its benefits, has been demonstrated by many research groups. In an effort to move closer toward a realized system, efforts must now be focused on critical issues that remain unsolved, namely instability of pattern classifiers over the course of hours and days.

Physiological cognitive state assessment: applications for designing effective human-machine systems.

Significant growth in the field of neuroscience has occurred over the last decade such that new application areas for basic research techniques are opening up to practitioners in many other areas. Of particular interest to many is the principle of neuroergonomics, by which the traditional work in neuroscience and its related topics can be applied to non-traditional areas such as human-machine system design. While work in neuroergonomics certainly predates the use of the term in the literature (previously identified by others as applied neuroscience, operational neuroscience, etc.

The effects of day-to-day variability of physiological data on operator functional state classification.

The application of pattern classification techniques to physiological data has undergone rapid expansion. Tasks as varied as the diagnosis of disease from magnetic resonance images, brain-computer interfaces for the disabled, and the decoding of brain functioning based on electrical activity have been accomplished quite successfully with pattern classification. These classifiers have been further applied in complex cognitive tasks to improve performance, in one example as an input to adaptive automation.