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)
Early stages in the development of a Brain-and-Body-Computer Interface controlled robot avatar are presented. The robot is aimed at performing well-defined daily tasks upon the choice and on behalf of a user. We built on recent advances in neuroscience, robotics and machine learning to demonstrate that it is possible to control a robot, accurately and(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. Prior research has suggested that embodied systems may ameliorate BCI end-user's experience and accuracy in controlling external(More)
OBJECTIVE This paper discusses the invariance and variability in interaction
 error-related potentials (ErrPs), where a special focus is laid upon the factors
 of (1) the human mental processing required to assess interface actions (2) time
 (3) subjects. APPROACH Three different experiments were designed as to vary
 primarily with respect(More)
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)
OBJECTIVE This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry application, and a way to enable fluent and intuitive interaction in embodiment systems. The main(More)
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