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Objective: Brain-Computer Interface technologies (BCI) enable the direct communication between humans and computers by analyzing brain measurements, such as electroencephalography (EEG). These technologies have been applied to a variety of domains, including neuroprosthetic control and the monitoring of epileptic seizures. Existing BCI systems primarily use(More)
Decades of heavy investment in laboratory-based brain imaging and neuroscience have led to foundational insights into how humans sense, perceive, and interact with the external world. However, it is argued that fundamental differences between laboratory-based and naturalistic human behavior may exist. Thus, it remains unclear how well the current knowledge(More)
OF TALK Engineers have long used control systems utilizing models and feedback loops to control real-world systems. Limitations of model-based control led to a generation of intelligent control techniques such as adaptive and fuzzy control. Human brain, on the other hand, is known to process a variety of inputs in parallel, ignore distractions to focus on(More)
Detecting significant periods of phase synchronization in EEG recordings is a non-trivial task that is made especially difficult when considering the effects of volume conduction and common sources. In addition, EEG signals are often confounded by non-neural signals, such as artifacts arising from muscle activity or external electrical devices. A variety of(More)
BACKGROUND Blind source separation techniques have become the de facto standard for decomposing electroencephalographic (EEG) data. These methods are poorly suited for incorporating prior information into the decomposition process. While alternative techniques to this problem, such as the use of constrained optimization techniques, have been proposed, these(More)
This paper introduces a new approach to develop robots that can learn general affordance relations from their experiences. Our approach is a part of larger efforts to develop a cognitive robot and has two components: (a) the robot models affordances as statistical relations among actions, object properties and the effects of actions on objects, in the(More)
– This paper discusses an application of cognitive control for robot task execution. The idea is currently being implemented in a humanoid robot ISAC using the Central Executive Agent and the Working Memory System. Using cognitive control, the robot should be able to learn how to execute task using past experience and emotion. This paper also discusses a(More)
BACKGROUND Recent neuroimaging analyses aim to understand how information is integrated across brain regions that have traditionally been studied in isolation; however, detecting functional connectivity networks in experimental EEG recordings is a non-trivial task. NEW METHOD We use neural mass models to simulate 10-s trials with coupling between 1-3 and(More)
Engineers have long used control systems utilizing models and feedback loops to control real-world systems. Limitations of model-based control led to a generation of intelligent control techniques such as adaptive and fuzzy control. Human brain, on the other hand, is known to process a variety of inputs in parallel, ignore distractions to focus on the task(More)