<div dir="ltr"><div dir="ltr"><div dir="ltr"><div><b>*** Frontiers research topic - Call for Contributions ***</b><br><br></div>We would like to invite contributions to the following research topic in Frontiers of Human Neuroscience:<br>"<a href="http://loop.frontiersin.org/researchtopic/5042" target="_blank">Detection and Estimation of Working Memory States and Cognitive Functions Based on Neurophysiological Measures</a>"<div><div><b><br></b>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.<b> </b>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 <a href="http://tinyurl.com/detectWM" target="_blank"><b>http://tinyurl.com/detectWM</b> </a>for more details and submission guidelines.<br><br>* Please let us know if you are <b>interested to contribute</b> by replying to <a href="mailto:felix.putze@uni-bremen.de" target="_blank">felix.putze@uni-bremen.de</a> <br><br><b>* Relevant Dates 31 January 2017</b>
- Abstract
<b><br> 30 April 2017</b>
- Manuscript<br><br></div><div><b>* Topic Editors </b> Felix Putze, University of Bremen, Germany<br></div><div> Fabien Lotte, Inria Bordeaux Sud-Ouest, France<br></div><div> Stephen Fairclough, Liverpool John Moores University, United Kingdom<br></div><div> Christian Mühl, German Aerospace Center, Cologne, Germany<br></div><div><br></div><div><b>* Topics of Interest</b><br><br></div><div>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.<br><br>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:<br><br>* 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?<br>* How can detection and classification of cognitive states be used in Brain-Computer Interfaces (BCIs)?<br>* How can multiple types of features or signal types be combined to achieve a more robust classification of working memory load?<br>*
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?<br>* How well can insights from offline, average-analysis studies on memory activity be transferred to online, single-trial BCIs?<br>*
How can models of working memory load be calibrated to account for
individual differences, for example in working memory capacity?<br>* How can approaches from computational cognitive modeling be combined with physiological signals to assess memory processes?<br>*
How can working memory load be classified, for example according to
modality (spatial memory, semantic memory, ...) or type of activity
(encoding, retrieval, rehearsal, ...)?<br>* How to design user-independent memory load estimators? Is that even feasible?<br>* How can memory load estimators from a given context or modality be transferred to another modality and/or context?<br>*
How can working memory activity be classified to predict the outcome of
the activity, for example by differentiating successful from failed
encoding attempts?<br><br>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.<br><br></div><div>With best regards,<br></div><div><br>Felix Putze,<br>Fabien Lotte,<br>Stephen Fairclough,<br>Christian Mühl.<br></div><div><br></div></div></div></div></div>