A　novel　EEGNet　variation　based　on　the　fusion　of　the　Inception　and　attention　mechanism　modules　was　proposed,　called　IncepA-EEGNet,　in　order　to　achieve　more　efficient　P300　signal　feature　extraction.　Convolutional　layers　with　different　receptive　fields　were　connected　in　parallel.　The　network＇s　ability　to　extract　and　express　EEG　signals　were　enhanced.　Then　the　attention　mechanism　was　introduced　to　assign　weights　to　the　features　of　different　filters,　and　important　information　was　extracted　from　the　P300　signal.　The　validation　experiment　was　conducted　on　two　subjects　of　BCI　Competition　III　dataset　II.　Results　showed　that　the　IncepA-EEGNet　recognition　accuracy　reached　75.5%　after　just　5　epochs　compared　with　other　deep　learning　models.　The　information　transmission　rate　was　up　to　33.44　bits/min　on　subject　B　after　3　epochs.　These　experimental　results　demonstrate　that　the　IncepA-EEGNet　effectively　improves　the　recognition　accuracy　of　the　P300　signal,　reduces　the　time　of　repeated　trials,　and　enhances　the　applicability　of　the　P300　speller.　Copyright　©2022　Journal　of　Zhejiang　University　(Engineering　Science).　All　rights　reserved.