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BACKGROUND For a self-paced motor imagery based brain-computer interface (BCI), the system should be able to recognize the occurrence of a motor imagery, as well as the type of the motor imagery. However, because of the difficulty of detecting the occurrence of a motor imagery, general motor imagery based BCI studies have been focusing on the cued motor(More)
We present a novel human-machine interface, called GOM-Face , and its application to humanoid robot control. The GOM-Face bases its interfacing on three electric potentials measured on the face: 1) glossokinetic potential (GKP), which involves the tongue movement; 2) electrooculogram (EOG), which involves the eye movement; 3) electromyogram, which involves(More)
In this paper we present an immersive brain computer interface (BCI) where we use a virtual reality head-mounted display (VRHMD) to invoke SSVEP responses. Compared to visual stimuli in monitor display, we demonstrate that visual stimuli in VRHMD indeed improve the user engagement for BCI. To this end, we validate our method with experiments on a VR maze(More)
Steady-state somatosensory evoked potential (SSSEP) is a recently developing brain-computer interface (BCI) paradigm where the brain response to tactile stimulation of a specific frequency is used. Generally, SSSEP-based BCI classifies whether the subjects focus on the tactile stimulation at the fingers in the left or right hand. In this paper, we examine(More)
Here, we report that the development of a brain-to-brain interface (BBI) system that enables a human user to manipulate rat movement without any previous training. In our model, the remotely-guided rats (known as ratbots) successfully navigated a T-maze via contralateral turning behaviour induced by electrical stimulation of the nigrostriatal (NS) pathway(More)
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