@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}
}
@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}
}
@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}
}