In　the　rapid　serial　visual　presentation　(RSVP)　classification　task,　the　data　from　the　target　and　non-target　classes　are　incredibly　imbalanced.　These　class　imbalance　problems　(CIPs)　can　hinder　the　classifier　from　achieving　better　performance,　especially　in　deep　learning.　This　paper　proposed　a　novel　data　augmentation　method　called　balanced　Wasserstein　generative　adversarial　network　with　gradient　penalty　(BWGAN-GP)　to　generate　RSVP　minority　class　data.　The　model　learned　useful　features　from　majority　classes　and　used　them　to　generate　minority-class　artificial　EEG　data.　It　combines　generative　adversarial　network　(GAN)　with　autoencoder　initialization　strategy　enables　this　method　to　learn　an　accurate　class-conditioning　in　the　latent　space　to　drive　the　generation　process　towards　the　minority　class.　We　used　RSVP　datasets　from　nine　subjects　to　evaluate　the　classification　performance　of　our　proposed　generated　model　and　compare　them　with　those　of　other　methods.　The　average　AUC　obtained　with　BWGAN-GP　on　EEGNet　was　94.43%,　an　increase　of　3.7%　over　the　original　data.　We　also　used　different　amounts　of　original　data　to　investigate　the　effect　of　the　generated　EEG　data　on　the　calibration　phase.　Only　60%　of　original　data　were　needed　to　achieve　acceptable　classification　performance.　These　results　show　that　the　BWGAN-GP　could　effectively　alleviate　CIPs　in　the　RSVP　task　and　obtain　the　best　performance　when　the　two　classes　of　data　are　balanced.　The　findings　suggest　that　data　augmentation　techniques　could　generate　artificial　EEG　to　reduce　calibration　time　in　other　brain-computer　interfaces　(BCI)　paradigms　similar　to　RSVP.