Suffering from dirty strong supersonic attacks

Suffering from dirty strong supersonic attacks (39ﾚｽ)
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8(1): YAMAGUTIseisei 2019/04/24(水)09:26 ID:5ZbN1Z79Q(7/32) BE AA×

8: YAMAGUTIseisei [sage] 2019/04/24(水) 09:26:24.26 ID:5ZbN1Z79Q BE:132176096-2BP(3)

The existing SSVEP-BCIs mainly rely on frequency recognition from EEG responses.
To develop a novel SSVEP-BCI paradigm for a brain-machine control system which allows users to continuously increase/decrease the moving speed of the application (i.e.
speed robot movements), this study hypothesizes that magnitude variation would help attain the goal.
Inspired by neuroscientific studies on human attention levels and SSVEP gains [13], feasibility studies are performed on the practicality of using SSVEP stimulus intensity to manipulate SSVEP magnitude.
In this experiment, the researchers varied the SSVEP stimulus intensity while keeping the stimulus frequency fixed.
Moreover, only a single-channel EEG is used here.
Using an experimental recorded EEG, the researchers conducted a comparative study of three predictive models for SSVEP magnitude variation.
Polynomial regression (Poly), random forest regression (RF), and neural network (NN) are proposed as potential models.
Leave-one-subject-out cross validation is performed to evaluate the mean square error (MSE) of prediction.
The results present that the predictive model for SSVEP magnitude variation using the RF approach outperforms both Poly and NN in terms of computational-time prediction with low MSE.
Finally, the merits of this study are demonstrated by streaming back the recorded EEG responses from the experiments into a brain-controlled robotic simulator.
A scenario is set to demonstrate the advantages of the proposed SSVEP-BCI paradigm over existing SSVEP-BCI systems.
Considered together in varying SSVEP stimulus frequency and intensity, the outcomes of this study are promising for further development of online SSVEP-BCI for smoothly controlled applications.

http://ai.2ch.sc/test/read.cgi/future/1511886855/8

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