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Performance of brain-computer interface (BCI) will depend, to a great extent, on the ability of subjects to control their own electroencephalographic signals (EEG). To this end, it is necessary to follow a suitable training and to provide some type of visual feedback. The objective of this study is to explore the possibility of improving the EEG control via feedback presentation. Eighteen untrained subjects, divided in two groups, were trained using a BCI system based on virtual reality techniques, which submits subjects to a more familiar environment, such as controlling a car to avoid different obstacles. Different types of obstacles were introduced for each group. Significant differences in classification error rates between both groups were obtained during the last second of the feedback period.
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