Suffering from dirty strong supersonic attacks

Suffering from dirty strong supersonic attacks (39ﾚｽ)
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28: YAMAGUTIseisei 2019/04/24(水)10:32 ID:5ZbN1Z79Q(27/32) BE AAS
The variation of visual stimulus intensity can help subjects to manipulate the SSVEP response magnitude.
A state-of-the-art machine learning approach, namely Random Forest Regression (RF) was proposed as the predictive model for handing SSVEP magnitude variation.
Leave-one-subject-out cross validation using the RF model showed the highest performance in the prediction of varying SSVEP magnitude compared to polynomial regression and neural networks models.
Thus, the RF model is promising for the further development of the proposed SSVEP-BCI in this study.
Even if an experiment has not yet been conducted on the proposed SSVEP-BCI system in an actual online mode,
the environmental and practical scenario demonstrated in the simulator, streaming back the actual brain signals from ten subjects, ensures that the proposed system is feasible and novel.
Through an online-like simulation, the system is evaluated in terms of speed, error, and smoothness from the brain-controlled robot in carrying the box to the destination.
Furthermore, the conceptual design of SSVEP stimulation is simple and user-friendly.
There are only three frequencies for the flickered stimuli on the screen with a single EEG channel, (Oz), for the measured brain signals.

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