★Suffering from dirty strong supersonic attacks

★Suffering from dirty strong supersonic attacks (39ﾚｽ)
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3: YAMAGUTIseisei 2019/04/24(水)09:17 ID:5ZbN1Z79Q(2/32) BE AAS
Abstract
The steady-state visual-evoked potential-based brain-computer interface (SSVEP-BCI) is a typically recognized visual stimulus frequency from brain responses.
Each frequency represents one command to control a machine.
For example, multiple target stimuli with different frequencies can be used to control the moving speeds of a robot.
Each target stimulus frequency corresponds to a speed level.
Such a conventional SSVEP-BCI is choice selection paradigm with discrete information, allowing users to discretely control the speed of a movable object.
This can result in non-smooth object movement.
To overcome the problem, in this study, a conceptual design of a SSVEP-BCI with continuous information for continuous control is proposed to allow users to control the moving speed of an object smoothly.
A predictive model for SSVEP magnitude variation plays an important role in the proposed design.
Thus, this study mainly focuses on a feasibility study concerning the use of SSVEP magnitude prediction for BCI.
A basic experiment is therefore conducted to gather SSVEP responses from varying stimulus intensity using times with a fixed frequency.
Random Forest Regression (RF) is outperformed by simple regression and neural networks in these predictive tasks.
Finally, the advantages of the proposed SSVEP-BCI is demonstrated by streaming real SSVEP responses from ten healthy subjects into a brain-controlled robotic simulator.
The results from this study show that the proposed SSVEP-BCI containing both frequency recognition and magnitude prediction is a promising approach for future continuous control applications.

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