TY - CHAP A1 - Krol, Laurens R. A1 - Zander, Thorsten O. ED - Fairclough, Stephen H. ED - Zander, Thorsten O. T1 - Defining neuroadaptive technology: the trouble with implicit human-computer interaction T2 - Current Research in Neuroadaptive Technology Y1 - 2022 SN - 978-0-12-821413-8 U6 - https://doi.org/10.1016/B978-0-12-821413-8.00007-5 SP - 17 EP - 42 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Krol, Laurens R. A1 - Klaproth, Oliver W. A1 - Vernaleken, Christoph A1 - Russwinkel, Nele A1 - Zander, Thorsten O. ED - Fairclough, Stephen H. ED - Zander, Thorsten O. T1 - Towards neuroadaptive modeling: assessing the cognitive states of pilots through passive brain-computer interfacing T2 - Current Research in Neuroadaptive Technology Y1 - 2022 SN - 978-0-12-821413-8 U6 - https://doi.org/10.1016/B978-0-12-821413-8.00009-9 SP - 59 EP - 73 PB - Elsevier CY - Amsterdam ER - TY - GEN A1 - Krol, Laurens R. A1 - Pawlitzki, Juliane A1 - Lotte, Fabien A1 - Gramann, Klaus A1 - Zander, Thorsten O. T1 - SEREEGA: Simulating event-related EEG activity T2 - Journal of Neuroscience Methods KW - General Neuroscience Y1 - 2018 U6 - https://doi.org/10.1016/j.jneumeth.2018.08.001 SN - 0165-0270 VL - 309 SP - 13 EP - 24 ER - TY - GEN A1 - Gallegos Ayala, Guillermo I. A1 - Haslacher, David A1 - Krol, Laurens R. A1 - Soekadar, Surjo R. A1 - Zander, Thorsten O. T1 - Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes T2 - Frontiers in Neuroergonomics N2 - 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. KW - Computer Networks and Communications KW - Hardware and Architecture KW - Software Y1 - 2023 U6 - https://doi.org/10.3389/fnrgo.2023.1233722 SN - 2673-6195 VL - 4 ER - TY - CHAP A1 - Pawlitzki, Juliane A1 - Krol, Laurens R. A1 - Zander, Thorsten O. ED - Fairclough, Stephen H. ED - Zander, Thorsten O. T1 - The impact of electrode shifts on BCI classifier accuracy T2 - Current Research in Neuroadaptive Technology Y1 - 2022 SN - 978-0-12-821413-8 U6 - https://doi.org/10.1016/B978-0-12-821413-8.00016-6 SP - 201 EP - 220 PB - Elsevier CY - Amsterdam ER - TY - GEN A1 - Zander, Thorsten O. A1 - Kothe, Christian T1 - Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general T2 - Journal of Neural Engineering KW - Cellular and Molecular Neuroscience KW - Biomedical Engineering Y1 - 2011 U6 - https://doi.org/10.1088/1741-2560/8/2/025005 SN - 1741-2560 VL - 8 IS - 2 ER - TY - GEN A1 - Krol, Laurens R. A1 - Haselager, Pim A1 - Zander, Thorsten O. T1 - Erratum: Cognitive and affective probing: a tutorial and review of active learning for neuroadaptive technology (2020 Journal of Neural Engineering 17.012001) T2 - Journal of Neural Engineering KW - Cellular and Molecular Neuroscience KW - Biomedical Engineering Y1 - 2020 U6 - https://doi.org/10.1088/1741-2552/ab8a6f SN - 1741-2552 VL - 17 IS - 4 ER - TY - GEN A1 - Zander, Thorsten O. A1 - Lehne, Moritz A1 - Ihme, Klas A1 - Jatzev, Sabine A1 - Correia, Joao A1 - Kothe, Christian A1 - Picht, Bernd A1 - Nijboer, Femke T1 - A Dry EEG-System for Scientific Research and Brain–Computer Interfaces T2 - Frontiers in Neuroscience KW - General Neuroscience Y1 - 2011 U6 - https://doi.org/10.3389/fnins.2011.00053 SN - 1662-4548 VL - 5 ER - TY - GEN A1 - Pfurtscheller, Gert A1 - Allison, Brendan Z. A1 - Brunner, Clemens A1 - Bauernfeind, Gunther A1 - Solis-Escalante, Teodoro A1 - Scherer, Reinhold A1 - Zander, Thorsten O. A1 - Mueller-Putz, Gernot A1 - Neuper, Christa A1 - Birbaumer, Niels T1 - The hybrid BCI T2 - Frontiers in Neuroscience KW - General Neuroscience Y1 - 2010 U6 - https://doi.org/10.3389/fnpro.2010.00003 SN - 1662-453X VL - 4 ER - TY - GEN A1 - Krol, Laurens R. A1 - Haselager, Pim A1 - Zander, Thorsten O. T1 - Cognitive and affective probing: a tutorial and review of active learning for neuroadaptive technology T2 - Journal of Neural Engineering KW - Cellular and Molecular Neuroscience KW - Biomedical Engineering Y1 - 2020 U6 - https://doi.org/10.1088/1741-2552/ab5bb5 SN - 1741-2552 VL - 17 IS - 1 ER - TY - GEN A1 - Zander, Thorsten O. A1 - Krol, Laurens R. A1 - Birbaumer, Niels P. A1 - Gramann, Klaus T1 - Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity T2 - Proceedings of the National Academy of Sciences N2 - 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. KW - Multidisciplinary Y1 - 2016 U6 - https://doi.org/10.1073/pnas.1605155114 SN - 0027-8424 VL - 113 IS - 52 SP - 14898 EP - 14903 ER - TY - GEN A1 - Zander, Thorsten O. A1 - Gaertner, Matti A1 - Kothe, Christian A1 - Vilimek, Roman T1 - Combining Eye Gaze Input With a Brain–Computer Interface for Touchless Human–Computer Interaction T2 - International Journal of Human-Computer Interaction KW - Computer Science Applications KW - Human-Computer Interaction KW - Human Factors and Ergonomics Y1 - 2010 U6 - https://doi.org/10.1080/10447318.2011.535752 SN - 1044-7318 VL - 27 IS - 1 SP - 38 EP - 51 ER - TY - GEN A1 - Andreessen, Lena M. A1 - Gerjets, Peter A1 - Meurers, Detmar A1 - Zander, Thorsten O. T1 - 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 T2 - User Modeling and User-Adapted Interaction N2 - 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. KW - Computer Science Applications KW - Human-Computer Interaction KW - Education Y1 - 2020 U6 - https://doi.org/10.1007/s11257-020-09273-5 SN - 0924-1868 VL - 31 IS - 1 SP - 75 EP - 104 ER - TY - GEN A1 - Pham Xuan, Rebecca A1 - Andreessen, Lena M. A1 - Zander, Thorsten O. T1 - Investigating the Single Trial Detectability of Cognitive Face Processing by a Passive Brain-Computer Interface T2 - Frontiers in Neuroergonomics N2 - 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. KW - Computer Networks and Communications KW - Hardware and Architecture KW - Software Y1 - 2022 U6 - https://doi.org/10.3389/fnrgo.2021.754472 SN - 2673-6195 VL - 2 ER - TY - GEN A1 - Klaproth, Oliver W. A1 - Dietz, Emmanuelle A1 - Pawlitzki, Juliane A1 - Krol, Laurens R. A1 - Zander, Thorsten O. A1 - Russwinkel, Nele T1 - Modeling of anticipation using instance-based learning: application to automation surprise in aviation using passive BCI and eye-tracking data T2 - User Modeling and User-Adapted Interaction KW - Computer Science Applications KW - Human-Computer Interaction KW - Education Y1 - 2024 U6 - https://doi.org/10.1007/s11257-024-09392-3 SN - 0924-1868 ER -