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>>30 > This is the first study on SSVEP magnitude prediction toward a novel SSVEP-BCI. > We created datasets from experiments on varying SSVEP magnitude responses. > The Random Forest Regression was then proposed as the algorithm for instantaneous SSVEP magnitude prediction. > The experimental results were obtained from ten subjects using leave-one-subject-out cross validation seem promising. > The instantaneous changes in predicted SSVEP magnitude can be mapped into the speed controller for brain-controlled applications (e.g. robot control). > Here, an online-like system was conducted using a simulated mobile robot. > The experiments involved streaming back the real SSVEP responses of varying magnitudes to control the moving speed of the robot. > For practical purposes, a single (Oz) EEG channel was used through all the experiments. > The advantage of the SSVEP magnitude prediction is that it has an ability to maintain stability when controlling the robotic. > In the near future, the outcomes from this work will be implemented in other smooth brain-controlled applications such as accelerating or decelerating the speed of a mobile robot or a robotic arm. > > > Page 10 > 10
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