[Eeglablist] Call for Papers: Cognitive State Assessment: Advances in Neuroergonomics

Daniel Roberts drobertc at gmu.edu
Tue Aug 11 12:27:56 PDT 2015

Please see the following call for papers, which I thought might be of
interest to some on the EEGLAB list:


Cognitive State Assessment: Advances in Neuroergonomics

Call for Papers

An increasing number of projects aim to assess the cognitive state of a
human user or operator. For example, cognitive state assessment is
beneficial for driving adaptive automation interfaces and for determining
whether or not a pilot or driver is experiencing high workload or is
fatigued or inattentive. Within a typical paradigm, machine-learning
methods are used to build a model to distinguish between two or more
cognitive states of interest, using data derived from human behavior or
physiology or both. While variations of such paradigms have reported
increasing success in inferring cognitive state with metrics such as EEG,
ECG, and eye tracking, several theoretical and practical issues remain.
Importantly, several of these issues are not faced within the larger field
of machine learning more generally and may require unique solutions from
experimentalists and practitioners working specifically within the domain
of cognitive state assessment.

Ongoing challenges include developing methods to reduce conceptual
circularity that may occur when the subjective report of the operator is
used as labels to train a machine learning model, when such a model is
intended to uncover a latent cognitive state. Additional challenges include
the utilization of data collection techniques or technologies that aim to
be less invasive on the user and developing techniques to improve the
interpretability of machine learning models, to allow for classification
paradigms to more usefully inform cognitive theory. Paradigms that improve
the generality of learned models, such that state assessment is maintained
across participants or across experimental sessions, are also highly sought
after. In this special issue we encourage papers, investigations,
algorithms, and classification techniques aimed at addressing any of these
key issues.

Potential topics include, but are not limited to:

Approaches to establish ground truth
Generalizable classifiers
Methods capitalizing on low cost wearable sensors
Increasing the interpretability of models
Machine learning techniques that improve over time
Classifiers that interact with automated systems
Methods of addressing limited data sets or poor signal quality

Authors can submit their manuscripts via the Manuscript Tracking System at

Manuscript Due Friday, 29 January 2016
First Round of Reviews Friday, 22 April 2016
Publication Date Friday, 17 June 2016
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