[Eeglablist] Frontiers Research Topic: Detection and Estimation of Working Memory States and Cognitive Functions Based on Neurophysiological Measures"

[Christian Muehl]

**** Frontiers research topic - Call for Contributions ****

We would like to invite contributions to the following research topic in
Frontiers of Human Neuroscience:
"Detection and Estimation of Working Memory States and Cognitive Functions
Based on Neurophysiological Measures
<http://loop.frontiersin.org/researchtopic/5042>"

Our objective is to publish a focused collection of open-access articles
that represent the state of the art in detection and estimation of working
memory and other cognitive functions based on neurophysiological signal
classification and aimed at the application of such classified states in
human-computer interaction. We specifically invite contributions that deal
with the detection of cognitive states in complex scenarios as they are
found in real world applications. Please refer to
*http://tinyurl.com/detectWM* <http://tinyurl.com/detectWM>for more details
and submission guidelines.

* Please let us know if you are *interested to contribute* by replying to
felix.putze at uni-bremen.de

** Relevant Dates     31 January 2017* -   Abstract
*                               30 April 2017*      -   Manuscript

** Topic Editors *       Felix Putze, University of Bremen, Germany
                               Fabien Lotte, Inria Bordeaux Sud-Ouest,
France
                               Stephen Fairclough, Liverpool John Moores
University, United Kingdom
                               Christian Mühl, German Aerospace Center,
Cologne, Germany

** Topics of Interest*

Executive cognitive functions like working memory determine the success or
failure of a wide variety of different cognitive tasks. Estimation of
constructs like working memory load or memory capacity from
neurophysiological or psychophysiological signals would enable adaptive
systems to respond to cognitive states experienced by an operator and
trigger responses designed to support task performance (e.g. by simplifying
the exercises of a tutor system, or by shutting down distractions from the
mobile phone). The determination of cognitive states like working memory
load is also useful for automated testing/assessment, for usability
evaluation and for tutoring applications. While there exists a huge body of
research work on neural and physiological correlates of cognitive functions
like working memory activity, fewer publications deal with the application
of this research with respect to single-trial detection and real-time
estimation of cognitive functions in complex, realistic scenarios.
Single-trial classifiers based on brain activity measurements such as EEG,
fNIRS or physiological signals such as EDA, ECG, BVP or Eyetracking have
the potential to classify affective or cognitive states based upon short
segments of data. For this purpose, signal processing and machine learning
techniques need to be developed and transferred to real-world user
interfaces.

In this research topic, we are looking for: (1) studies in complex,
realistic scenarios that specifically deal with cognitive states or
cognitive processes (memory-related or other), (2) classification and
estimation of cognitive states and processes like working memory activity,
and (3) applications to Brain-Computer Interfaces and Human-Computer
Interaction in general. Central open research questions which we would like
to see approached in this research topic comprise:

* How can working memory load be quantified with regression or
classification models which are robust to perturbations common to realistic
recording conditions and natural brain signal fluctuations?
* How can detection and classification of cognitive states be used in
Brain-Computer Interfaces (BCIs)?
* How can multiple types of features or signal types be combined to achieve
a more robust classification of working memory load?
* How can working memory activity be differentiated from other types of
cognitive or affective activity which co-vary with, but are not related to
memory?
* How well can insights from offline, average-analysis studies on memory
activity be transferred to online, single-trial BCIs?
* How can models of working memory load be calibrated to account for
individual differences, for example in working memory capacity?
* How can approaches from computational cognitive modeling be combined with
physiological signals to assess memory processes?
* How can working memory load be classified, for example according to
modality (spatial memory, semantic memory, ...) or type of activity
(encoding, retrieval, rehearsal, ...)?
* How to design user-independent memory load estimators? Is that even
feasible?
* How can memory load estimators from a given context or modality be
transferred to another modality and/or context?
* How can working memory activity be classified to predict the outcome of
the activity, for example by differentiating successful from failed
encoding attempts?

Additionally, we are also interested in other relevant submissions related
to the research topic. We also sincerely invite manuscripts dealing with
applications of memory-related interfaces (e.g. adaptive human-computer
interfaces for memory-intensive tasks). Comprehensive review articles which
critically reflect the state-of-the-art on a certain aspect of the topic
are also welcome.

With best regards,

Felix Putze,
Fabien Lotte,
Stephen Fairclough,
Christian Mühl.
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