Investigating Ensemble Learning and Classifier Generalization in a Hybrid, Passive Brain-Computer Interface for Assessing Cognitive Workload.

Hybrid, passive brain-computer (h/pBCI) interfaces have received much attention in regards to measuring various mental states. A high classification rate of operator workload state is necessary in order to be able to enhance operator performance. Physiological measures have been used with machine learning algorithms to classify workload state, however, these measures are hypothesized to suffer from inherent nonstationarity.

The effect of propensity to trust and perceptions of trustworthiness on trust behaviors in dyads.

Research on trust has burgeoned in the last few decades. Despite the growing interest in trust, little is known about trusting behaviors in non-dichotomous trust games. The current study explored propensity to trust, trustworthiness, and trust behaviors in a new computer-mediated trust relevant task.

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.

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.