Christian I. Penaloza

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This paper addresses the problem of mental fatigue caused by prolonged use of Brain Machine Interface (BMI) Systems. We propose a system that gradually becomes autonomous by learning user preferences and by considering error perception feedback. As a particular application, we show that our system allows patients to control electronic appliances in a(More)
In this research we present a non-invasive Brain-Machine Interface (BMI) system that allows patients with motor paralysis conditions to control electronic appliances in a hospital room. The novelty of our system compared to other BMI applications is that our system gradually becomes autonomous by learning user actions (i.e. turning on/off window, lights,(More)
We present our method for learning object categories from the internet using cues obtained through human-robot interaction. Such cues include an object model acquired by observation and the name of the object. Our learning approach emulates the natural learning process of children when they observe their environment, encounter unknown objects and ask adults(More)
The experiment described in this paper is performed within a system that provides a human with the possibility and capability to be physically immersed in the body of an android robot, Geminoid HI-2. The participant, through the swapped body of Geminoid HI-2, is able to see his/her own body in front of themselves like a reflection in a mirror -- then they(More)
This research proposes an integral system combining a hybrid BCI interface and shared control system for navigation and manipulation applications. In particular, the system consists of an electrical wheelchair with an embedded robotic arm that can assist a user to achieve essencial tasks such as picking up a cup of water. The proposed system uses a graphic(More)