TY  - JOUR
A1  - Miralles, Felip
A1  - Vargiu, Eloisa
A1  - Dauwalder, Stefan
A1  - Solà, Marc
A1  - Müller-Putz, Gernot
A1  - Wriessnegger, Selina C.
A1  - Pinegger, Andreas
A1  - Kübler, Andrea
A1  - Halder, Sebastian
A1  - Käthner, Ivo
A1  - Martin, Suzanne
A1  - Daly, Jean
A1  - Armstrong, Elaine
A1  - Guger, Christoph
A1  - Hintermüller, Christoph
A1  - Lowish, Hannah
T1  - Brain computer interface on track to home.
JF  - The Scientific World Journal
N2  - The novel BackHome system offers individuals with disabilities a range of useful services available via brain-computer interfaces (BCIs), to help restore their independence. This is the time such technology is ready to be deployed in the real world, that is, at the target end users’ home. This has been achieved by the development of practical electrodes, easy to use software, and delivering telemonitoring and home support capabilities which have been conceived, implemented, and tested within a user-centred design approach. The final BackHome system is the result of a 3-year long process involving extensive user engagement to maximize effectiveness, reliability, robustness, and ease of use of a home based BCI system. The system is comprised of ergonomic and hassle-free BCI equipment; one-click software services for Smart Home control, cognitive stimulation, and web browsing; and remote telemonitoring and home support tools to enable independent home use for nonexpert caregivers and users. BackHome aims to successfully bring BCIs to the home of people with limited mobility to restore their independence and ultimately improve their quality of life.
KW  - brain computer interface
KW  - disability
KW  - limited mobility
KW  - independence
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-149575
VL  - 2015
IS  - 623896
ER  - 
TY  - JOUR
A1  - Scherer, Reinhold
A1  - Faller, Josef
A1  - Friedrich, Elisabeth V. C.
A1  - Opisso, Eloy
A1  - Costa, Ursula
A1  - Kübler, Andrea
A1  - Müller-Putz, Gernot R.
T1  - Individually Adapted Imagery Improves Brain-Computer Interface Performance in End-Users with Disability
JF  - PLoS ONE
N2  - 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.
KW  - single-trial EEG classification
KW  - motor imagery technology
KW  - spatial filters movement
KW  - communication systems
KW  - BCI
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-143021
VL  - 10
IS  - 5
ER  -