@phdthesis{Andreessen2023, author = {Andreeßen, Lena M.}, title = {Towards real-world applicability of neuroadaptive technologies : investigating subject-independence, task-independence and versatility of passive brain-computer interfaces}, doi = {10.26127/BTUOpen-6652}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-66521}, school = {BTU Cottbus - Senftenberg}, year = {2023}, abstract = {Brain-computer interfacing (BCI) is a relatively new field of study that involves engineering, neuroscience, psychology, and physiology. A BCI is a system that allows direct communication between the brain and the environment, using signals from electroencephalography (EEG) to identify a user's intentions and mental states. This can be used for human-machine interaction (HMI) to adapt software or control a device. Particularly passive BCIs are the most promising for the general population, as they can detect and interpret a user's mental state without requiring their attention. This can enable neuroadaptive technology (NAT), which allows a machine to learn over time how the user perceives and interprets the world. However, there are still obstacles to be overcome before BCI technology can be applied in real-world situations for NAT. In this work I firstly address the issue of subject dependence in training data collection for classifier calibration, which can be time-consuming and impractical. I investigated the training of a subject-independent predictive model that is trained on a group of other users' data and applied to online-testing data of a new person. This was done in the context of training an automatic classifier for error detection and correction. Results showed that a classifier model can be trained without user-specific calibration and with high accuracy. The number of electrodes used in training the model was also reduced. Further it was validated that the trained classifier models were based on cortical sources and not other modalities. In a second study I address the issue of task-dependence. To that end I tested the application of a potentially task-independent calibration paradigm for mental workload assessment in a new task. This new task was a speed reading context, where subjects read texts of varying difficulty and speed. The study found that the mental workload prediction model was accurate in classifying mental workload in different reading tasks, indicating that it can be used as a task-independent classifier for mental workload. In a third study I examine if it is possible to measure neural correlates of human moral assessment using a passive BCI on a single-trial basis. A calibration paradigm was developed using pictures that were ranked as morally unacceptable and morally neutral. However, the results showed low classification accuracies and it was not possible to reliably distinguish between a user's subjective moral evaluations on a single-trial basis using current classification approaches. The results presented in this thesis provide solutions towards real-world applicability of NAT enabled by passive BCIs, as examples of a subject-independent and a task-independent classifier are demonstrated and discussed. Further, approaches for increased versatility of passive BCI technology are presented, that could make passive BCIs more feasible for use in future real-world human-machine interaction settings.}, subject = {Passive brain-computer interfaces; Mental state assessment; Neuroadaptive technology; Human-machine interaction; Electroencephalography; Passive Gehirn-Computer-Schnittstellen; Neuroadaptive Technologie; Mensch-Maschine-Interaktion; Elektroenzephalographie; Erfassung mentaler Zust{\"a}nde; Elektroencephalographie; Hirnfunktion; Mensch-Maschine-Interaktion; Gehirn-Computer-Schnittstelle}, language = {en} }