@article{ErlbeckMochtyKuebleretal.2017,
  author    = {Erlbeck, Helena and Mochty, Ursula and K{\"u}bler, Andrea and Real, Ruben G. L.},
  title     = {Circadian course of the P300 ERP in patients with amyotrophic lateral sclerosis - implications for brain-computer interfaces (BCI)},
  series = {BMC Neurology},
  volume    = {17},
  journal   = {BMC Neurology},
  number    = {3},
  doi       = {10.1186/s12883-016-0782-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-157423},
  year      = {2017},
  abstract  = {Background: Accidents or neurodegenerative diseases like amyotrophic lateral sclerosis (ALS) can lead to progressing, extensive, and complete paralysis leaving patients aware but unable to communicate (locked-in state). Brain-computer interfaces (BCI) based on electroencephalography represent an important approach to establish communication with these patients. The most common BCI for communication rely on the P300, a positive deflection arising in response to rare events. To foster broader application of BCIs for restoring lost function, also for end-users with impaired vision, we explored whether there were specific time windows during the day in which a P300 driven BCI should be preferably applied. Methods: The present study investigated the influence of time of the day and modality (visual vs. auditory) on P300 amplitude and latency. A sample of 14 patients (end-users) with ALS and 14 healthy age matched volunteers participated in the study and P300 event-related potentials (ERP) were recorded at four different times (10, 12 am, 2, \& 4 pm) during the day. Results: Results indicated no differences in P300 amplitudes or latencies between groups (ALS patients v. healthy participants) or time of measurement. In the auditory condition, latencies were shorter and amplitudes smaller as compared to the visual condition. Conclusion: Our findings suggest applicability of EEG/BCI sessions in patients with ALS throughout normal waking hours. Future studies using actual BCI systems are needed to generalize these findings with regard to BCI effectiveness/efficiency and other times of day.},
  language  = {en}
}
@article{SchererFallerFriedrichetal.2015,
  author    = {Scherer, Reinhold and Faller, Josef and Friedrich, Elisabeth V. C. and Opisso, Eloy and Costa, Ursula and K{\"u}bler, Andrea and M{\"u}ller-Putz, Gernot R.},
  title     = {Individually Adapted Imagery Improves Brain-Computer Interface Performance in End-Users with Disability},
  series = {PLoS ONE},
  volume    = {10},
  journal   = {PLoS ONE},
  number    = {5},
  doi       = {10.1371/journal.pone.0123727},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-143021},
  pages     = {e0123727},
  year      = {2015},
  abstract  = {Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75\%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair- wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e. g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within- day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15\% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.},
  language  = {en}
}
@phdthesis{Kaethner2015,
  author    = {K{\"a}thner, Ivo R. J.},
  title     = {Auditory and visual brain-computer interfaces as communication aids for persons with severe paralysis},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-135477},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2015},
  abstract  = {Brain-computer interfaces (BCIs) could provide a muscle-independent communication channel to persons with severe paralysis by translating brain activity into device commands. As a means of communication, in particular BCIs based on event-related potentials (ERPs) as control signal have been researched. Most of these BCIs rely on visual stimulation and have been investigated with healthy participants in controlled laboratory environments. In proof-of-principle studies targeted end users gained control over BCI systems; however, these systems are not yet established as an assistive technology for persons who would most benefit from them. The main aim of this thesis is to advance the usability of ERP-BCIs for target users. To this end, five studies with BCIs have been conducted that enabled users to communicate by focusing their attention on external stimuli. Two studies were conducted in order to demonstrate the advantages and to further improve the practical application of visual BCIs. In the first study, mental workload was experimentally manipulated during prolonged BCI operation. The study showed the robustness of the visual ERP-BCI since users maintained a satisfactory level of control despite constant distraction in the form of background noise. Moreover, neurophysiological markers that could potentially serve as indicators of high mental workload or fatigue were revealed. This is a first step towards future applications in which the BCI could adapt to the mental state of the user (e.g. pauses if high mental workload is detected to prevent false selections). In the second study, a head-mounted display (HMD), which assures that stimuli are presented in the field of view of the user, was evaluated. High accuracies and information transfer rates, similar to a conventional display, were achieved by healthy participants during a spelling task. Furthermore, a person in the locked-in state (LIS) gained control over the BCI using the HMD. The HMD might be particularly suited for initial communication attempts with persons in the LIS in situations, where mounting a conventional monitor is difficult or not feasible. Visual ERP-BCIs could prove valuable for persons with residual control over eye muscles and sufficient vision. However, since a substantial number of target users have limited control over eye movements and/or visual impairments, BCIs based on non-visual modalities are required. Therefore, a main aspect of this thesis was to improve an auditory paradigm that should enable motor impaired users to spell by focusing attention on different tones. The two conducted studies revealed that healthy participants were able to achieve high spelling performance with the BCI already in the first session and stress the importance of the choice of the stimulus material. The employed natural tones resulted in an increase in performance compared to a previous study that used artificial tones as stimuli. Furthermore, three out of five users with a varying degree of motor impairments could gain control over the system within the five conducted sessions. Their performance increased significantly from the first to the fifth session - an effect not previously observed for visual ERP-BCIs. Hence, training is particularly important when testing auditory multiclass BCIs with potential users. A prerequisite for user satisfaction is that the BCI technology matches user requirements. In this context, it is important to compare BCIs with already established assistive technology. Thus, the fifth study of this dissertation evaluated gaze dependent methods (EOG, eye tracking) as possible control signals for assistive technology and a binary auditory BCI with a person in the locked-in state. The study participant gained control over all tested systems and rated the ease of use of the BCI as the highest among the tested alternatives, but also rated it as the most tiring due to the high amount of attention that was needed for a simple selection. Further efforts are necessary to simplify operation of the BCI. The involvement of end users in all steps of the design and development process of BCIs will increase the likelihood that they can eventually be used as assistive technology in daily life. The work presented in this thesis is a substantial contribution towards the goal of re-enabling communication to users who cannot rely on motor activity to convey their thoughts.},
  subject      = {Gehirn-Computer Schnittstelle},
  language  = {en}
}
@article{HalderVarkutiBogdanetal.2013,
  author    = {Halder, Sebastian and Varkuti, Balint and Bogdan, Martin and K{\"u}bler, Andrea and Rosenstiel, Wolfgang and Sitaram, Ranganatha and Birbaumer, Niels},
  title     = {Prediction of brain-computer interface aptitude from individual brain structure},
  series = {Frontiers in Human Neuroscience},
  journal   = {Frontiers in Human Neuroscience},
  doi       = {10.3389/fnhum.2013.00105},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-96558},
  year      = {2013},
  abstract  = {Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Results: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Conclusions: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. Significance: This confirms that structural brain traits contribute to individual performance in BCI use.},
  language  = {en}
}
@phdthesis{Kaufmann2013,
  author    = {Kaufmann, Tobias},
  title     = {Brain-computer interfaces based on event-related potentials: toward fast, reliable and easy-to-use communication systems for people with neurodegenerative disease},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-83441},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2013},
  abstract  = {Objective: Brain Computer Interfaces (BCI) provide a muscle independent interaction channel making them particularly valuable for individuals with severe motor impairment. Thus, different BCI systems and applications have been proposed as assistive technology (AT) solutions for such patients. The most prominent system for communication utilizes event-related potentials (ERP) obtained from the electroencephalogram (EEG) to allow for communication on a character-by-character basis. Yet in their current state of technology, daily life use cases of such systems are rare. In addition to the high EEG preparation effort, one of the main reasons is the low information throughput compared to other existing AT solutions. Furthermore, when testing BCI systems in patients, a performance drop is usually observed compared to healthy users. Patients often display a low signal-to-noise ratio of the recorded EEG and detection of brain responses may be aggravated due to internally (e.g. spasm) or externally induced artifacts (e.g. from ventilation devices). Consequently, practical BCI systems need to cope with mani-fold inter-individual differences. Whilst these high demands lead to increasing complexity of the technology, daily life use of BCI systems requires straightforward setup including an easy-to-use graphical user interface that nonprofessionals can handle without expert support. Research questions of this thesis: This dissertation project aimed at bringing forward BCI technology toward a possible integration into end-users' daily life. Four basic research questions were addressed: (1) Can we identify performance predictors so that we can provide users with individual BCI solutions without the need of multiple, demanding testing sessions? (2) Can we provide complex BCI technology in an automated, user-friendly and easy-to-use manner, so that BCIs can be used without expert support at end-users' homes? (3) How can we account for and improve the low information transfer rates as compared to other existing assistive technology solutions? (4) How can we prevent the performance drop often seen when bringing BCI technology that was tested in healthy users to those with severe motor impairment? Results and discussion: (1) Heart rate variability (HRV) as an index of inhibitory control (i.e. the ability to allocate attention resources and inhibit distracting stimuli) was significantly related to ERP-BCI performance and accounted for almost 26\% of variance. HRV is easy to assess from short heartbeat recordings and may thus serve as a performance predictor for ERP-BCIs. Due to missing software solutions for appropriate processing of artifacts in heartbeat data (electrocardiogram and inter-beat interval data), our own tool was developed that is available free of charge. To date, more than 100 researchers worldwide have requested the tool. Recently, a new version was developed and released together with a website (www.artiifact.de). (2) Furthermore, a study of this thesis demonstrated that BCI technology can be incorporated into easy-to-use software, including auto-calibration and predictive text entry. Na{\"i}ve, healthy nonprofessionals were able to control the software without expert support and successfully spelled words using the auto-calibrated BCI. They reported that software handling was straightforward and that they would be able to explain the system to others. However, future research is required to study transfer of the results to patient samples. (3) The commonly used ERP-BCI paradigm was significantly improved. Instead of simply highlighting visually displayed characters as is usually done, pictures of famous faces were used as stimulus material. As a result, specific brain potentials involved in face recognition and face processing were elicited. The event-related EEG thus displayed an increased signal-to-noise ratio, which facilitated the detection of ERPs extremely well. Consequently, BCI performance was significantly increased. (4) The good results of this new face-flashing paradigm achieved with healthy participants transferred well to users with neurodegenerative disease. Using a face paradigm boosted information throughput. Importantly, two users who were highly inefficient with the commonly used paradigm displayed high accuracy when exposed to the face paradigm. The increased signal-to-noise ratio of the recorded EEG thus helped them to overcome their BCI inefficiency. Significance: The presented work at hand (1) successfully identified a physiological predictor of ERP-BCI performance, (2) proved the technology ready to be operated by na{\"i}ve nonprofessionals without expert support, (3) significantly improved the commonly used spelling paradigm and (4) thereby displayed a way to effectively prevent BCI inefficiency in patients with neurodegenerative disease. Additionally, missing software solutions for appropriate handling of artifacts in heartbeat data encouraged development of our own software tool that is available to the research community free of charge. In sum, this thesis significantly improved current BCI technology and enhanced our understanding of physiological correlates of BCI performance.},
  subject      = {Gehirn-Computer-Schnittstelle},
  language  = {en}
}