Electroencephalogram　(EEG)　based　brain-computer　interfaces　(BCIs)　are　becoming　popular　in　clinical　diagnosis　applications.　However,　this　raises　a　new　issue　on　the　robustness　of　deep　neural　networks-based　BCIs　against　environmental　noise　and　adversarial　attacks.　Unfortunately,　there　is　no　adversarial　defense　approach　tailored　for　EEG　adversarial　robustness　so　far.　In　this　work,　we　systematically　evaluate　the　performance　of　5　popular　adversarial　training　(AT)-based　defense　approaches　on　three　large-scale　and　real-world　EEG　datasets　with　3　popular　EEG　classification　models,　under　3　different　white-box　attacks.　Through　extensive　experiments,　we　demonstrate　that　the　naïve　AT　is　a　promising　adversarial　defense　approach　in　EEG-based　BCIs.　However,　existing　regularization　terms　originated　from　vision　tasks　do　not　generalize　well　to　EEG　signals.　Our　results　shed　light　on　the　future　development　of　the　EEG　adversarial　defense　approach.　©　2022　IEEE.