L
Laurens R. Krol
Researcher at Technical University of Berlin
Publications - 30
Citations - 543
Laurens R. Krol is an academic researcher from Technical University of Berlin. The author has contributed to research in topics: Cognition & Computer science. The author has an hindex of 10, co-authored 27 publications receiving 365 citations. Previous affiliations of Laurens R. Krol include Brandenburg University of Technology & Eindhoven University of Technology.
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Proceedings ArticleDOI
Meyendtris: a hands-free, multimodal tetris clone using eye tracking and passive BCI for intuitive neuroadaptive gaming
TL;DR: A completely hands-free version of Tetris that uses eye tracking and passive brain-computer interfacing (a real-time measurement and interpretation of brain activity) to replace existing game elements, as well as introduce novel ones.
Journal ArticleDOI
Hybrid brain-computer interface with motor imagery and error-related brain activity
TL;DR: This work shows for the first time, that the error-related brain activity classifier compared to the motor imagery classifier is more consistent when trained on calibration data and tested during online control, which likely explains why the proposed hybrid BCI allows for a more reliable means of communication or rehabilitation for patients in need.
Journal ArticleDOI
Good scientific practice in EEG and MEG research: Progress and perspectives
Guiomar Niso,Laurens R. Krol,Etienne Combrisson,Anne-Sophie Dubarry,Madison Elliott,Clément François,Yseult Héjja-Brichard,Sophie K. Herbst,Karim Jerbi,Vanja Kovic,Katia Lehongre,Steven J. Luck,Manuel R. Mercier,John C. Mosher,Yuri G. Pavlov,Aina Puce,Antonio Schettino,Daniele Schön,Walter Sinnott-Armstrong,Bertille Somon,Anđela Šoškić,Suzy J. Styles,Roni Tibon,Martina G. Vilas,Marijn van Vliet,Maximilien Chaumon +25 more
TL;DR: Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization as discussed by the authors .
Journal ArticleDOI
Automated Labeling of Movement- Related Cortical Potentials Using Segmented Regression
TL;DR: The proposed segmented regression along with a local peak method for automated labeling of the movement-related cortical potential features can be used to automatically obtain robust estimates for the MRCP features with known measurement error.
Proceedings ArticleDOI
A task-independent workload classifier for neuroadaptive technology: Preliminary data
TL;DR: Preliminary data is presented demonstrating it is possible to calibrate a task-independent classifier to identify when a user is under heavy workload across different activities, using different types of mental arithmetic and even a semantic task.