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Due to the relative noise and artifact insensitivity, steady-state visual evoked potential (SSVEP) has been used increasingly in the study of a brain-computer interface (BCI). However, SSVEP is still influenced by the same frequency component in the spontaneous EEG, and it is meaningful to find a parameter that can avoid or decrease this influence to improve the transfer rate and the accuracy of the SSVEP-based BCI. In this work, with wavelet analysis, a new parameter named stability coefficient (SC) was defined to measure the stability of a frequency, and then the electrode with the highest stability was selected as the signal electrode for further analysis. After that, the SC method and the traditional power spectrum (PS) method were used comparatively to recognize the stimulus frequency from an analogous BCI data constructed from a real SSVEP data, and the results showed that the SC method is better for a short time window data.

In photoacoustic imaging (PAI), the photoacoustic (PA) signal can be observed only from limit-view angles due to some structure limitations. As a result, data incompleteness artifacts appear and some image details lose. An arc-direction mask in PA data acquisition and arc-direction compressed sensing (CS) reconstruction algorithm are proposed instead of the conventional rectangle CS methods for PAI. The proposed method can effectively realize the compression of the PA data along the arc line and exactly recover the PA images from multi-angle observation. Simulation results demonstrate that it has the potential of application in high-resolution PAI for obtaining highly resolution and artifact-free PA images.

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