@misc{GallegosAyalaHaslacherKroletal., author = {Gallegos Ayala, Guillermo I. and Haslacher, David and Krol, Laurens R. and Soekadar, Surjo R. and Zander, Thorsten O.}, title = {Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes}, series = {Frontiers in Neuroergonomics}, volume = {4}, journal = {Frontiers in Neuroergonomics}, issn = {2673-6195}, doi = {10.3389/fnrgo.2023.1233722}, abstract = {Brain-computer interfaces (BCI) can provide real-time and continuous assessments of mental workload in different scenarios, which can subsequently be used to optimize human-computer interaction. However, assessment of mental workload is complicated by the task-dependent nature of the underlying neural signals. Thus, classifiers trained on data from one task do not generalize well to other tasks. Previous attempts at classifying mental workload across different cognitive tasks have therefore only been partially successful. Here we introduce a novel algorithm to extract frontal theta oscillations from electroencephalographic (EEG) recordings of brain activity and show that it can be used to detect mental workload across different cognitive tasks. We use a published data set that investigated subject dependent task transfer, based on Filter Bank Common Spatial Patterns. After testing, our approach enables a binary classification of mental workload with performances of 92.00 and 92.35\%, respectively for either low or high workload vs. an initial no workload condition, with significantly better results than those of the previous approach. It, nevertheless, does not perform beyond chance level when comparing high vs. low workload conditions. Also, when an independent component analysis was done first with the data (and before any additional preprocessing procedure), even though we achieved more stable classification results above chance level across all tasks, it did not perform better than the previous approach. These mixed results illustrate that while the proposed algorithm cannot replace previous general-purpose classification methods, it may outperform state-of-the-art algorithms in specific (workload) comparisons.}, language = {en} } @incollection{KrolZander, author = {Krol, Laurens R. and Zander, Thorsten O.}, title = {Defining neuroadaptive technology: the trouble with implicit human-computer interaction}, series = {Current Research in Neuroadaptive Technology}, booktitle = {Current Research in Neuroadaptive Technology}, editor = {Fairclough, Stephen H. and Zander, Thorsten O.}, publisher = {Elsevier}, address = {Amsterdam}, isbn = {978-0-12-821413-8}, doi = {10.1016/B978-0-12-821413-8.00007-5}, pages = {17 -- 42}, language = {en} } @incollection{KrolKlaprothVernalekenetal., author = {Krol, Laurens R. and Klaproth, Oliver W. and Vernaleken, Christoph and Russwinkel, Nele and Zander, Thorsten O.}, title = {Towards neuroadaptive modeling: assessing the cognitive states of pilots through passive brain-computer interfacing}, series = {Current Research in Neuroadaptive Technology}, booktitle = {Current Research in Neuroadaptive Technology}, editor = {Fairclough, Stephen H. and Zander, Thorsten O.}, publisher = {Elsevier}, address = {Amsterdam}, isbn = {978-0-12-821413-8}, doi = {10.1016/B978-0-12-821413-8.00009-9}, pages = {59 -- 73}, language = {en} } @misc{KrolPawlitzkiLotteetal., author = {Krol, Laurens R. and Pawlitzki, Juliane and Lotte, Fabien and Gramann, Klaus and Zander, Thorsten O.}, title = {SEREEGA: Simulating event-related EEG activity}, series = {Journal of Neuroscience Methods}, volume = {309}, journal = {Journal of Neuroscience Methods}, issn = {0165-0270}, doi = {10.1016/j.jneumeth.2018.08.001}, pages = {13 -- 24}, language = {en} } @incollection{PawlitzkiKrolZander, author = {Pawlitzki, Juliane and Krol, Laurens R. and Zander, Thorsten O.}, title = {The impact of electrode shifts on BCI classifier accuracy}, series = {Current Research in Neuroadaptive Technology}, booktitle = {Current Research in Neuroadaptive Technology}, editor = {Fairclough, Stephen H. and Zander, Thorsten O.}, publisher = {Elsevier}, address = {Amsterdam}, isbn = {978-0-12-821413-8}, doi = {10.1016/B978-0-12-821413-8.00016-6}, pages = {201 -- 220}, language = {en} } @misc{KrolHaselagerZander, author = {Krol, Laurens R. and Haselager, Pim and Zander, Thorsten O.}, title = {Erratum: Cognitive and affective probing: a tutorial and review of active learning for neuroadaptive technology (2020 Journal of Neural Engineering 17.012001)}, series = {Journal of Neural Engineering}, volume = {17}, journal = {Journal of Neural Engineering}, number = {4}, issn = {1741-2552}, doi = {10.1088/1741-2552/ab8a6f}, language = {en} } @misc{KrolHaselagerZander, author = {Krol, Laurens R. and Haselager, Pim and Zander, Thorsten O.}, title = {Cognitive and affective probing: a tutorial and review of active learning for neuroadaptive technology}, series = {Journal of Neural Engineering}, volume = {17}, journal = {Journal of Neural Engineering}, number = {1}, issn = {1741-2552}, doi = {10.1088/1741-2552/ab5bb5}, language = {en} } @misc{ZanderKrolBirbaumeretal., author = {Zander, Thorsten O. and Krol, Laurens R. and Birbaumer, Niels P. and Gramann, Klaus}, title = {Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity}, series = {Proceedings of the National Academy of Sciences}, volume = {113}, journal = {Proceedings of the National Academy of Sciences}, number = {52}, issn = {0027-8424}, doi = {10.1073/pnas.1605155114}, pages = {14898 -- 14903}, abstract = {The human brain continuously and automatically processes information concerning its internal and external context. We demonstrate the elicitation and subsequent detection and decoding of such "automatic interpretations" by means of context-sensitive probes in an ongoing human-computer interaction. Through a sequence of such probe-interpretation cycles, the computer accumulates responses over time to model the operator's cognition, even without that person being aware of it. This brings human cognition directly into the human-computer interaction loop, expanding traditional notions of "interaction." The concept introduces neuroadaptive technology—technology which automatically adapts to an estimate of its operator's mindset. This technology bears relevance to autoadaptive experimental designs, and opens up paradigm-shifting possibilities for human-machine systems in general.}, language = {en} } @misc{KlaprothDietzPawlitzkietal., author = {Klaproth, Oliver W. and Dietz, Emmanuelle and Pawlitzki, Juliane and Krol, Laurens R. and Zander, Thorsten O. and Russwinkel, Nele}, title = {Modeling of anticipation using instance-based learning: application to automation surprise in aviation using passive BCI and eye-tracking data}, series = {User Modeling and User-Adapted Interaction}, journal = {User Modeling and User-Adapted Interaction}, issn = {0924-1868}, doi = {10.1007/s11257-024-09392-3}, language = {en} }