With the recent advancement of the Brain-Computer Interface (BCI), Electroencephalogram (EEG) analytics gain a lot of research attention from various domains. Understanding the vulnerabilities of EEG analytics is important for safely applying this emerging technology in our daily life. Recent studies show that EEG analytics are vulnerable to adversarial attacks when adding small perturbations on the EEG data. However, fewer research efforts have been devoted to the robustness of EEG analytics under sparse perturbations that attack only small portions of the data. In this paper, we conduct the first in-depth study on the robustness of EEG analytics under sparse perturbations and propose the first Sparse Adversarial eeG Attack, SAGA, to identify weakness of EEG analytics. Specifically, by viewing EEG data as time series collected from several channels, we design an adaptive mask to uniformly represent diverse sparsity in adversarial attacks. We further introduce a PGD-based iterative solver to automatically select the time steps and channels under the given sparsity constraints and effectively identify the adversarial examples on EEG data. Extensive experiments show that SAGA can effectively generate sparse perturbations and introduces a 77.02% accuracy drop on average by only perturbing 5% channels and time steps.