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Steady-state visual evoked potentials (SSVEPs) are widely employed for target detection in brain-computer interfaces (BCIs). Canonical correlation analysis (CCA), which extends ordinary correlation to two sets of variables, is a new method for SSVEP detection. In this paper, the performance of CCA is compared with that of traditional power spectral density analysis (PSDA) in terms of power spectral amplitude, recognition accuracy, information transfer rate and operating speed. The results show that the CCA method outperforms the PSDA in all these technical indexes.Keywords-brain-computer interface; steady-state visual evoked potentials; canonical correlation analysis; power spectral density analysis * Corresponding author: wqg07@163.com minimal training, and provides high transfer rate [5], it is the first choice of BCI input signals, in order to implement a practical BCI system.

Density-functional theory calculations are performed to investigate the effects of surface modifications and nanosheet thickness on the electronic and magnetic properties of gallium nitride (GaN) nanosheets (NSs). Unlike the bare GaN NSs terminating with polar surfaces, the systems with hydrogenated Ga (H-GaN), fluorinated Ga (F-GaN), and chlorinated Ga (Cl-GaN) preserve their initial wurtzite structures and exhibit ferromagnetic states. The abovementioned three different decorations on Ga atoms are energetically more favorable for thicker GaN NSs. Moreover, as the thickness increases, H-GaN and F-GaN NSs undergo semiconductor to metal and half-metal to metal transition, respectively, while Cl-GaN NSs remain completely metallic. The predicted diverse and tunable electronic and magnetic properties highlight the potential of GaN NSs for novel electronic and spintronic nanodevices.