Recently, steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has significantly progressed and is moving from the laboratory to practical application. However, the system performance and ...
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Recently, steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has significantly progressed and is moving from the laboratory to practical application. However, the system performance and comfort of SSVEP-BCIs still need to be improved. In this study, five flicker frequencies (i.e., 30-34 Hz with an interval of 1 Hz) and eight scaling frequencies (i.e., 0.4-1.8 Hz with an interval of 0.2 Hz) were adopted to jointly encode forty visual stimulus targets using evoked intermodulation (IM) frequency components. Both luminance and shape changes are implemented by sinusoidal sampling stimulus coding methods. High-frequency flicker frequencies and green visual stimuli were chosen to improve the comfort of the proposed system. An extended version of a training algorithm named task-discriminant component analysis (TDCA) was proposed to detect the IM components of SSVEP signals. The average recognition accuracy of eleven subjects is 96.82 f 0.01 % in the offline experiments for a data length of 5 s. Online validation experiments was constructed from the optimized parameters of offline analysis, and the average accuracy and ITR were 94.37 f 1.17 % and 113.47 f 2.60 bits/ min, respectively. Furthermore, ten subjects who participated in the validation part also completed the online free-spell task successfully. These results showed that it is feasible to expand the number of stimulus targets by using IM frequency components of SSVEP signals for target coding, and that the system performance is superior.
Objective. With the recent development of visual evoked potential (VEP) based brain-computer interfaces (BCIs), the stimulus paradigm has been continuously innovated, in which the pursuit of higher BCI performance and...
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Objective. With the recent development of visual evoked potential (VEP) based brain-computer interfaces (BCIs), the stimulus paradigm has been continuously innovated, in which the pursuit of higher BCI performance and better user experience has become indispensable. Approach. To optimize the stimulus paradigm, a 12-target online BCI system was designed in this study by adopting flicker for steady-state VEPs, Newton's ring for steady-state motion VEP, and frame rate based video stimulus, respectively. The signal characteristics of VEP, classification accuracy, and user experience of the three stimulus paradigms were quantitatively evaluated and compared. Main results. The online information transfer rates for the three stimulus paradigms were 53.77 bits min-1, 51.41 +/- 3.55 bits min-1, and 52.07 +/- 3.09 bits min-1, respectively. The video stimulus had a significantly better user experience, while the flicker stimulus showed the worst. Significance. These results demonstrate the advantage of the proposed video stimulus paradigm and have significant theoretical and applied implications for developing VEP-based BCI systems.
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