Zhang, XixieKrol, Laurens R.Zander, Thorsten O.2021-01-252021-01-252019-01-17978-1-5386-6650-0978-1-5386-6651-71062-922Xhttps://depositonce.tu-berlin.de/handle/11303/12516http://dx.doi.org/10.14279/depositonce-11335Passive Brain-Computer-Interfaces provide a promising approach to the continuous measurement of mental workload in realistic scenarios. Typically, a BCI is calibrated to discriminate between different levels of workload induced by a specific task. However, workload in realistic scenarios is typically a result of a mixture of different tasks. Here, we present a study on investigating the possibility of a task-independent classifier, which can be applied to classify mental workload induced by various tasks (including n-back, backward span, addition, word recovery and mental rotation). Furthermore, our approach is not limited to binary classification of workload but can discriminate it on a continuous metric.en150 Psychologiepassive Brain-Computer-Interfacesneuroadaptive classifiertask-independent technologymental workloadcontinuous metricConference Object2577-1655