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EEG-based neural signatures of different types of working-memory load

Working groupMultimodal Interaction Lab
Duration01/2017 - open
FundingIWM budget resources
Project description

The project aims at bridging the gap between neuroscience and instructional psychology via informatics by studying the use of brain-computer interface (BCI) methodologies for research in instructional psychology (e.g., the individual classification of working memory load in real-time). As long-term goal stands the development of an online adaptive learning environment based on physiological measures of basic cognitive processes. Specifically, the online adaption grounds on the detection and classification of specific neural signatures in the electroencephalogram (EEG) of learners by means of advanced BCI methodologies.

Within the project specific signatures of the EEG (e.g., EEG alpha and theta frequency band power) for working memory and attentional processes that are hypothesized to be elementary in complex learning tasks are studied. Additionally the project focuses on eye-tracking measures like the pupil dilation (i.e., the increase in pupil diameter under increasing cognitive load). The goal of the project is to gain insight in the specificity and sensitivity of EEG and eye-tracking measures for these elementary cognitive processes and to develop a task that allows the combined manipulation of load on these cognitive processes. This task will be used to train specific BCI-classifiers (i.e., computational pattern-recognition algorithms). By means of cross-task-classification the hypothesis that elementary cognitive processes are building blocks of complex processes during learning is examined. Cross-task classification means that BCI-classifiers are trained and validated on simple, well-controlled working memory or attentional tasks and then used on learning material to detect the specific working memory load signatures. A potential learning environment might use this technology to assess the cognitive load of learners in real-time and to adapt the learning material accordingly (i.e., by increasing or decreasing task difficulty) to maintain an optimal level of cognitive load for each learner.

The project has been part of the Tuebinger Leibniz-WissenschaftsCampus 'Informational Environments' (01/2013 – 12/2016). Since 01/2017 the project is continued in cooperation with the Wilhelm-Schickard-Institute for Informatics, University of Tuebingen (Prof. Rosenstiel, Dr. Martin Spueler, Tanja Krumpe).


Wilhelm-Schickard-Institute for Informatics, University of Tübingen


Scharinger, C., Kammerer, Y., & Gerjets, P. (2015). Pupil dilation and EEG alpha frequency band power reveal load on executive functions for link-selection processes during text reading. PLoS ONE, 10, e0130608.

Scharinger, C., Soutschek, A., Schubert, T., & Gerjets, P. (2015). When flanker meets the n-back: What EEG and pupil dilation data reveal about the interplay between the two central-executive working memory functions inhibition and updating. Psychophysiology, 52, 1293-1304.

Gerjets, P., Walter, C., Rosenstiel, W., Bogdan, M., & Zander, T. O. (2014). Cognitive state monitoring and the design of adaptive instruction in digital environments: Lessons learned from cognitive workload assessment using a passive brain-computer interface approach. Frontiers in Neuroscience, 8:385. doi:10.3389/fnins.2014.00385.