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30: YAMAGUTIseisei 2019/04/24(水)10:34 ID:5ZbN1Z79Q(29/32) BE AAS
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.
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