★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 AAS
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.
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