@article{will_p62._2018,
	title = {P62. {SSVEP} controlled {BCI} inferring complex tasks from low-level-commands},
	volume = {129},
	issn = {1388-2457},
	url = {http://www.sciencedirect.com/science/article/pii/S1388245718310071},
	doi = {https://doi.org/10.1016/j.clinph.2018.04.697},
	abstract = {Background Brain-computer-interfaces (BCIs) aim to give mobility to motion-disabled people, therefore they are used to control external devices sending low-level-commands to manipulate single degrees of freedom (DOFs) or high-level commands to directly reach for pre-defined targets. Low-level commands are not suitable for complex tasks where multiple DOFs are manipulated, as information transfer rates are low in general, whereas high-level commands enable complex tasks but lack free navigation (Sakurada et al., 2013, Diez et al., 2011). So far approaches combining both low- and high-level commands have been applied for spelling devices, allowing users to select single letters allong with the opportunity to automatically complete words (Saa et al., 2015). This study investigates the possibility to apply a combined approach to control movable objects. Methods Brain activity is measured with EEG in a steady-state-visual-evoked-potential (SSVEP) experiment. Canonical correlation analysis (CCA) is used for feature generation. Classification is conducted by applying a Naive Bayes approach. The experimental setup contains of 5 stimuli, 4 of which are associated to moving a cursor in a 2D space, one is used to automatically reach a predicted target. Target prediction is based on the extrapolation of the cursors trajectory. Results Classification achieved high recognition rates. Targets could be infered successfully from the trajectory of the cursor. Once the right target was predicted automatic reaching could be used. As a result, targets were attained substantially faster than with non-automatic reaching. Additionally, users were granted the possibility to cancel automatic cursor movement in case they changed their mind about the target. Significance The investigated approach enables control of different movable objects (e.g. a robotic arm or a wheelchair) in a combined low-level and high-level command fashion, closing the gap between free navigation and the possibility to automatically attain a specific target. This study serves as a working proof-of-concept for a new, more natural BCI control for movable objects. Funding BMBF and FC STIMULATE (13GW0095A).},
	number = {8},
	journal = {Clinical Neurophysiology},
	author = {Will, Matthias and Pfeiffer, Tim and Heinze, Nicolai and Rose, Georg},
	year = {2018},
	pages = {e92 -- e93}
}