Algorithm 1 The OCLS method for SSVEP-BCI

Input:    Individual calibration data: ${\mathit{X}}_{e,1}\in {\mathbb{R}}^{N_c\times N_s{N}_f \times { N}_t }$, Regularization parameter: $\alpha$,    Selection boundary: $\beta$,    Maximum number of iterations: max_iteration    Convergence tolerance: e Process:    Computing ${\mathit{S}}_{e,1}$ by (2) and ${\mathit{Q}}_{e,1}$ by (3)    Obtaining the initial spatial filter ${\mathit{w}}_{e,1}$ by TRCA    Computing space distance $\mathit{D}$ by (13)    Constructing optimization objective $J$ by (14)    Constructing derivative $\dot{J}$ by (16)    Sparse learning: $\widehat{\mathit{w}}$ ← minFunc ($J$, $\dot{J}$,${\mathit{w}}_{e,1}$, max_iteration, e) $Cs$ ← optimal channel selection by (17)

$\mathbf{Output}:\mathrm{Optimal} \mathrm{channel} \mathrm{set} Cs$