Mohammad Abu-Alqumsan

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A brain-computer interface (BCI) translates brain activity into commands to control devices or software. Common approaches are based on visual evoked potentials (VEP), extracted from the electroencephalogram (EEG) during visual stimulation. High information transfer rates (ITR) can be achieved using (i) steady-state VEP (SSVEP) or (ii) code-modulated VEP(More)
Mobile visual location recognition needs to be performed in real-time for location based services to be perceived as useful. We describe and validate an approach that eliminates the network delay by preloading partial visual vocabularies to the mobile device. Retrieval performance is significantly increased by composing partial vocabularies based on the(More)
The development of technological applications that allow people to control and embody external devices within social interaction settings represents a major goal for current and future brain-computer interface (BCI) systems.
OBJECTIVE Spatial filtering has proved to be a powerful pre-processing step in detection of steady-state visual evoked potentials and boosted typical detection rates both in offline analysis and online SSVEP-based brain-computer interface applications. State-of-the-art detection methods and the spatial filters used thereby share many common foundations as(More)
nformation about the location, orientation, and context of a mobile device is of central importance for future multimedia applications and location-based services (LBSs). With the widespread adoption of modern camera phones, including powerful processors, inertial measurement units, compass, and assisted global positioning system (GPS) receivers, the(More)
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