@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} } @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{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{ZanderKothe, author = {Zander, Thorsten O. and Kothe, Christian}, title = {Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general}, series = {Journal of Neural Engineering}, volume = {8}, journal = {Journal of Neural Engineering}, number = {2}, issn = {1741-2560}, doi = {10.1088/1741-2560/8/2/025005}, 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{ZanderLehneIhmeetal., author = {Zander, Thorsten O. and Lehne, Moritz and Ihme, Klas and Jatzev, Sabine and Correia, Joao and Kothe, Christian and Picht, Bernd and Nijboer, Femke}, title = {A Dry EEG-System for Scientific Research and Brain-Computer Interfaces}, series = {Frontiers in Neuroscience}, volume = {5}, journal = {Frontiers in Neuroscience}, issn = {1662-4548}, doi = {10.3389/fnins.2011.00053}, language = {en} } @misc{PfurtschellerAllisonBrunneretal., author = {Pfurtscheller, Gert and Allison, Brendan Z. and Brunner, Clemens and Bauernfeind, Gunther and Solis-Escalante, Teodoro and Scherer, Reinhold and Zander, Thorsten O. and Mueller-Putz, Gernot and Neuper, Christa and Birbaumer, Niels}, title = {The hybrid BCI}, series = {Frontiers in Neuroscience}, volume = {4}, journal = {Frontiers in Neuroscience}, issn = {1662-453X}, doi = {10.3389/fnpro.2010.00003}, 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{ZanderGaertnerKotheetal., author = {Zander, Thorsten O. and Gaertner, Matti and Kothe, Christian and Vilimek, Roman}, title = {Combining Eye Gaze Input With a Brain-Computer Interface for Touchless Human-Computer Interaction}, series = {International Journal of Human-Computer Interaction}, volume = {27}, journal = {International Journal of Human-Computer Interaction}, number = {1}, issn = {1044-7318}, doi = {10.1080/10447318.2011.535752}, pages = {38 -- 51}, language = {en} } @misc{AndreessenGerjetsMeurersetal., author = {Andreessen, Lena M. and Gerjets, Peter and Meurers, Detmar and Zander, Thorsten O.}, title = {Toward neuroadaptive support technologies for improving digital reading: a passive BCI-based assessment of mental workload imposed by text difficulty and presentation speed during reading}, series = {User Modeling and User-Adapted Interaction}, volume = {31}, journal = {User Modeling and User-Adapted Interaction}, number = {1}, issn = {0924-1868}, doi = {10.1007/s11257-020-09273-5}, pages = {75 -- 104}, abstract = {We investigated whether a passive brain-computer interface that was trained to distinguish low and high mental workload in the electroencephalogram (EEG) can be used to identify (1) texts of different readability difficulties and (2) texts read at different presentation speeds. For twelve subjects we calibrated a subject-dependent, but task-independent predictive model classifying mental workload. We then recorded EEG data from each subject, while twelve texts in blocks of three were presented to them word by word. Half of the texts were easy, and the other half were difficult texts according to classic reading formulas. From each text category three texts were read at a self-adjusted comfortable presentation speed and the other three at an increased speed. For each subject we applied the predictive model to EEG data of each word of the twelve texts. We found that the resulting predictive values for mental workload were higher for difficult texts than for easy texts. Predictive values from texts presented at an increased speed were also higher than for those presented at a normal self-adjusted speed. The results suggest that the task-independent predictive model can be used on single-subject level to build a highly predictive user model of the reader over time. Such a model could be employed in a system which continuously monitors brain activity related to mental workload and adapts to specific reader's abilities and characteristics by adjusting the difficulty of text materials and the way it is presented to the reader in real time. A neuroadaptive system like this could foster efficient reading and text-based learning by keeping readers' mental workload levels at an individually optimal level.}, language = {en} } @misc{PhamXuanAndreessenZander, author = {Pham Xuan, Rebecca and Andreessen, Lena M. and Zander, Thorsten O.}, title = {Investigating the Single Trial Detectability of Cognitive Face Processing by a Passive Brain-Computer Interface}, series = {Frontiers in Neuroergonomics}, volume = {2}, journal = {Frontiers in Neuroergonomics}, issn = {2673-6195}, doi = {10.3389/fnrgo.2021.754472}, abstract = {An automated recognition of faces enables machines to visually identify a person and to gain access to non-verbal communication, including mimicry. Different approaches in lab settings or controlled realistic environments provided evidence that automated face detection and recognition can work in principle, although applications in complex real-world scenarios pose a different kind of problem that could not be solved yet. Specifically, in autonomous driving—it would be beneficial if the car could identify non-verbal communication of pedestrians or other drivers, as it is a common way of communication in daily traffic. Automated identification from observation whether pedestrians or other drivers communicate through subtle cues in mimicry is an unsolved problem so far, as intent and other cognitive factors are hard to derive from observation. In contrast, communicating persons usually have clear understanding whether they communicate or not, and such information is represented in their mindsets. This work investigates whether the mental processing of faces can be identified through means of a Passive Brain-Computer Interface (pBCI). This then could be used to support the cars' autonomous interpretation of facial mimicry of pedestrians to identify non-verbal communication. Furthermore, the attentive driver can be utilized as a sensor to improve the context awareness of the car in partly automated driving. This work presents a laboratory study in which a pBCI is calibrated to detect responses of the fusiform gyrus in the electroencephalogram (EEG), reflecting face recognition. Participants were shown pictures from three different categories: faces, abstracts, and houses evoking different responses used to calibrate the pBCI. The resulting classifier could distinguish responses to faces from that evoked by other stimuli with accuracy above 70\%, in a single trial. Further analysis of the classification approach and the underlying data identified activation patterns in the EEG that corresponds to face recognition in the fusiform gyrus. The resulting pBCI approach is promising as it shows better-than-random accuracy and is based on relevant and intended brain responses. Future research has to investigate whether it can be transferred from the laboratory to the real world and how it can be implemented into artificial intelligences, as used in autonomous driving.}, 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} }