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Reinforcement learning (RL) can automate a wide variety of robotic skills, but learning each new skill requires considerable real-world data collection and manual representation engineering to design policy classes or features. Using deep reinforcement learning to train general purpose neural network policies alleviates some of the burden of manual representation engineering by using expressive policy...
Artifacts such as voluntarily and involuntarily muscle movements are usually seen as a source of noise in EEG signals. In this paper, we see artifacts as a source of information in a signal. For example, eye movements can generate a traceable change in the EEG signals. We use eye movements as an effective marker for direction of movement. We propose two experiments for classification of four eye movement...
This paper presents a non-invasive Brain Computer Interface (BCI) game that is inspired on the Tetris game. The BCI-Tetris is presented in three different versions. Two versions based on the P300 event related potential (ERP), and one version that combines the P300 ERP with the control of sensorimotor rhythms. The BCI-Tetris is being developed to be tested in pilot experiments with children with attention-deficit...
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