Exploring　the　effective　signal　features　of　electroencephalogram　(EEG)　signals　is　an　important　issue　in　the　research　of　brain-computer　interface　(BCI),　and　the　results　can　reveal　the　motor　intentions　that　trigger　electrical　changes　in　the　brain,　which　has　broad　research　prospects　for　feature　extraction　from　EEG　data.　In　contrast　to　previous　EEG　decoding　methods　that　are　based　solely　on　a　convolutional　neural　network,　the　traditional　convolutional　classification　algorithm　is　optimized　by　combining　a　transformer　mechanism　with　a　constructed　end-to-end　EEG　signal　decoding　algorithm　based　on　swarm　intelligence　theory　and　virtual　adversarial　training.　The　use　of　a　self-attention　mechanism　is　studied　to　expand　the　receptive　field　of　EEG　signals　to　global　dependence　and　train　the　neural　network　by　optimizing　the　global　parameters　in　the　model.　The　proposed　model　is　evaluated　on　a　real-world　public　dataset　and　achieves　the　highest　average　accuracy　of　63.56%　in　cross-subject　experiments,　which　is　significantly　higher　than　that　found　for　recently　published　algorithms.　Additionally,　good　performance　is　achieved　in　decoding　motor　intentions.　The　experimental　results　show　that　the　proposed　classification　framework　promotes　the　global　connection　and　optimization　of　EEG　signals,　which　can　be　further　applied　to　other　BCI　tasks.