@article{KueblerHolzRiccioetal.2014,
  author    = {K{\"u}bler, Andrea and Holz, Elisa M. and Riccio, Angela and Zickler, Claudia and Kaufmann, Tobias and Kleih, Sonja C. and Staiger-S{\"a}lzer, Pit and Desideri, Lorenzo and Hoogerwerf, Evert-Jan and Mattia, Donatella},
  title     = {The User-Centered Design as Novel Perspective for Evaluating the Usability of BCI-Controlled Applications},
  doi       = {10.1371/journal.pone.0112392},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-111051},
  year      = {2014},
  abstract  = {Albeit research on brain-computer interfaces (BCI) for controlling applications has expanded tremendously, we still face a translational gap when bringing BCI to end-users. To bridge this gap, we adapted the user-centered design (UCD) to BCI research and development which implies a shift from focusing on single aspects, such as accuracy and information transfer rate (ITR), to a more holistic user experience. The UCD implements an iterative process between end-users and developers based on a valid evaluation procedure. Within the UCD framework usability of a device can be defined with regard to its effectiveness, efficiency, and satisfaction. We operationalized these aspects to evaluate BCI-controlled applications. Effectiveness was regarded equivalent to accuracy of selections and efficiency to the amount of information transferred per time unit and the effort invested (workload). Satisfaction was assessed with questionnaires and visual-analogue scales. These metrics have been successfully applied to several BCI-controlled applications for communication and entertainment, which were evaluated by end-users with severe motor impairment. Results of four studies, involving a total of N = 19 end-users revealed: effectiveness was moderate to high; efficiency in terms of ITR was low to high and workload low to medium; depending on the match between user and technology, and type of application satisfaction was moderate to high. The here suggested evaluation metrics within the framework of the UCD proved to be an applicable and informative approach to evaluate BCI controlled applications, and end-users with severe impairment and in the locked-in state were able to participate in this process.},
  language  = {en}
}
@article{KueblerBlankertzKleihetal.2014,
  author    = {K{\"u}bler, Andrea and Blankertz, Benjamin and Kleih, Sonja C. and Kaufmann, Tobias and Hammer, Eva M.},
  title     = {Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR)},
  doi       = {10.3389/fnhum.2014.00574},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-113084},
  year      = {2014},
  abstract  = {Modulation of sensorimotor rhythms (SMR) was suggested as a control signal for brain-computer interfaces (BCI). Yet, there is a population of users estimated between 10 to 50\% not able to achieve reliable control and only about 20\% of users achieve high (80-100\%) performance. Predicting performance prior to BCI use would facilitate selection of the most feasible system for an individual, thus constitute a practical benefit for the user, and increase our knowledge about the correlates of BCI control. In a recent study, we predicted SMR-BCI performance from psychological variables that were assessed prior to the BCI sessions and BCI control was supported with machine-learning techniques. We described two significant psychological predictors, namely the visuo-motor coordination ability and the ability to concentrate on the task. The purpose of the current study was to replicate these results thereby validating these predictors within a neurofeedback based SMR-BCI that involved no machine learning.Thirty-three healthy BCI novices participated in a calibration session and three further neurofeedback training sessions. Two variables were related with mean SMR-BCI performance: (1) a measure for the accuracy of fine motor skills, i.e., a trade for a person's visuo-motor control ability; and (2) subject's "attentional impulsivity". In a linear regression they accounted for almost 20\% in variance of SMR-BCI performance, but predictor (1) failed significance. Nevertheless, on the basis of our prior regression model for sensorimotor control ability we could predict current SMR-BCI performance with an average prediction error of M = 12.07\%. In more than 50\% of the participants, the prediction error was smaller than 10\%. Hence, psychological variables played a moderate role in predicting SMR-BCI performance in a neurofeedback approach that involved no machine learning. Future studies are needed to further consolidate (or reject) the present predictors.},
  language  = {en}
}
@article{KaufmannHerwegKuebler2014,
  author    = {Kaufmann, Tobias and Herweg, Andreas and K{\"u}bler, Andrea},
  title     = {Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials},
  doi       = {10.1186/1743-0003-11-7},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-110042},
  year      = {2014},
  abstract  = {Background People with severe disabilities, e.g. due to neurodegenerative disease, depend on technology that allows for accurate wheelchair control. For those who cannot operate a wheelchair with a joystick, brain-computer interfaces (BCI) may offer a valuable option. Technology depending on visual or auditory input may not be feasible as these modalities are dedicated to processing of environmental stimuli (e.g. recognition of obstacles, ambient noise). Herein we thus validated the feasibility of a BCI based on tactually-evoked event-related potentials (ERP) for wheelchair control. Furthermore, we investigated use of a dynamic stopping method to improve speed of the tactile BCI system. Methods Positions of four tactile stimulators represented navigation directions (left thigh: move left; right thigh: move right; abdomen: move forward; lower neck: move backward) and N = 15 participants delivered navigation commands by focusing their attention on the desired tactile stimulus in an oddball-paradigm. Results Participants navigated a virtual wheelchair through a building and eleven participants successfully completed the task of reaching 4 checkpoints in the building. The virtual wheelchair was equipped with simulated shared-control sensors (collision avoidance), yet these sensors were rarely needed. Conclusion We conclude that most participants achieved tactile ERP-BCI control sufficient to reliably operate a wheelchair and dynamic stopping was of high value for tactile ERP classification. Finally, this paper discusses feasibility of tactile ERPs for BCI based wheelchair control.},
  language  = {en}
}