Xiaoyang Li's research while affiliated with Tsinghua University and other places
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Publications (2)
Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been widely studied due to the high information transfer rate (ITR), little user training, and wide subject applicability. However, there are also disadvantages such as visual discomfort and “BCI illiteracy.” To address these problems, this study proposes to us...
In order to explore the effect of low frequency stimulation on pupil size and electroencephalogram (EEG), we presented subjects with 1-6Hz black-and-white-alternating flickering stimulus, and compared the differences of signal-to-noise ratio (SNR) and classification performance between pupil size and visual evoked potentials (VEPs). The results sho...
Citations
... Thus, a CCA-based spatial filter can be obtained and then the 8-channel data were processed by the CCA-based spatial filter. Lastly, the amplitude spectrum y(f) was calculated by fast Fourier Transform (FFT), and the SNR in decibels (dB) was defined as the ratio of y(f) to the mean value of the eight neighboring frequencies [40]. The flow chart of the calculation process is shown in Figure 3. One-way repeated-measures analysis of variance (ANOVA) was also used to test the difference of amplitude and SNR between the PreG electrode and the wet electrode at different frequencies. ...
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... This study intends to use low-frequency visual stimulations that can simultaneously elicit VEP and PR (Jiang et al., 2020) to implement a 12-target h-BCI speller. Compared with the existing HCI and BCI work related to PR, this system aims to achieve a shorter detection time and higher classification accuracy by adopting efficient coding and decoding methods. ...
