★Suffering from dirty strong supersonic attacks (39ﾚｽ)
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29: YAMAGUTIseisei 2019/04/24(水)10:33 ID:5ZbN1Z79Q(28/32) BE AAS
As the research findings on the predictive model for SSVEP magnitude variation indicate, the predictive SSVEP magnitude paradigm can now be integrated into the frequency recognition paradigm to achieve a novel online SSVEP-BCI.
Taking into consideration both the frequency and magnitude of the steady brain responses, a continuous SSVEP-BCI can be provided, allowing the users to smoothly control devices (e.g. a mobile robot).
Furthermore, we are planning to integrate the proposed SSVEP-BCI to handle robotic arms in an online mode using sparse EEG channels.
The continuous increase or decrease of the predicted signal from the predictive SSVEP magnitude can be mapped into command functions, for instance, by accelerating or decelerating the velocity of the robotic arm.
To improve performance of the proposed SSVEP-BCI, a robustness to noises for the continuous magnitude prediction is an important.
To overcome this issue, one approach is a simple adaptive algorithm which measures magnitude information from not only target SSVEP frequency, but also from the neighbor frequencies.
Using relative values instead of an absolute value from target frequency, we hypothesize that the predictive model possibly classifies weather magnitude variation is an effect from noises or actual varying in SSVEP response.
Another approach, we are going to apply distributed recurrent neural forward model which behaves as a temporal memory module to maintain continuous information as closed as uncorrupted SSVEP responses [19].
Hence, the contribution of this work can act as a gateway to future BCI-based control.
VI.
CONCLUSION
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