Steady-state visual evoked potential, type of electroencephalography (EEG) signal, that is used for brain–computer interface systems are considered in this Letter. Steady-state visual evoked potential stimulator is needed for realising the signal on the scalp. Besides, information transfer rate is the most significant parameter to evaluate overall performance of a brain–computer interface. EEG signal classification methods, task completion time, and signal stimulator structure affect information transfer rate values. In this Letter, the authors aimed to reach a high information transfer rate by using the proposed signal stimulator and classification method that has new architectures. Eight flickering objects that provide 36 different characters to spell were used. This stimuli optimisation prevented the effect of eye fatigue on signal. Therefore, steady-state visual evoked potential was elicited dominantly. Moreover, 1D convolutional neural network for signal classification was proposed in this Letter. Online experimental data was also classified with canonical correlation analysis that is most commonly used in brain–computer interface systems. The authors compared results according to both of the classification methods. They have reached average value of information transfer rate as 50.67 bit/min with the proposed classification method. This result is significantly higher than similar studies.

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Advanced SSVEP stimulator for brain–computer interface and signal classification with using convolutional neural network

References

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