2021
DOI: 10.1101/2021.02.09.430407
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Linking Labs: Interconnecting Experimental Environments

Abstract: We introduce the concept of LabLinking: a technology-based interconnection of experimental laboratories across institutions, disciplines, cultures, languages, and time zones - in other words experiments without borders. In particular, we introduce LabLinking levels (LLL), which define the degree of tightness of empirical interconnection between labs. We describe the technological infrastructure in terms of hard- and software required for the respective LLLs and present examples of linked laboratories along wit… Show more

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Cited by 1 publication
(3 citation statements)
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“…To overcome this issue, we employ the LabLinking paradigm [18] for our experiment, depicted in Figure 1. In this paradigm for experimental research, it is possible for the robot Pepper to remain at its usual location at Bielefeld University while interacting in real-time with a human participant at the BioSignals Lab at the University of Bremen.…”
Section: Lablinking Methodsmentioning
confidence: 99%
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“…To overcome this issue, we employ the LabLinking paradigm [18] for our experiment, depicted in Figure 1. In this paradigm for experimental research, it is possible for the robot Pepper to remain at its usual location at Bielefeld University while interacting in real-time with a human participant at the BioSignals Lab at the University of Bremen.…”
Section: Lablinking Methodsmentioning
confidence: 99%
“…Through a shallow grid search, we obtained optimized results for all participants with a parameter setting of 500 trees, a maximum tree depth of 10, a minimum sample split of 3, and a minimum of 4 samples per leaf. Further, a combination of all features from the delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), low beta (12)(13)(14)(15)(16)(17)(18)(19)(20), and high beta (20-30 Hz) bands binned in 2 Hz led to the optimal performance for 4-12 Hz in the first classification task (ambient vs. distraction) and for 4-20 Hz in the second classification (distraction vs. hesitation), and 62 × 5 = 310 and 62 × 9 = 558 dimensional feature vectors, respectively.…”
Section: Classificationmentioning
confidence: 99%
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