Objective: To assess the feasibility of recognizing visual spatial attention frames for Brain-computer interfaces (BCI) applications. Methods: EEG data was recorded with 64 electrodes from 2 subjects executing a visual spatial attention task indicating 2 target locations. Continuous Morlet wavelet coefficients were estimated on 18 frequency components and 16 preselected electrodes in trials of 600 ms. The spatial patterns of the 16 frequency components frames were simultaneously detected and classified (between the two targets). The classification accuracy was assessed using 20-fold crossvalidation. Results: The maximum frames average classification accuracies are 80.64% and 87.31% for subject 1 and 2 respectively, both utilizing coefficients estimated at frequencies located in gamma band.