BackgroundThe　effective　analysis　methods　for　steady-state　visual　evoked　potential　(SSVEP)　signals　are　critical　in　supporting　an　early　diagnosis　of　glaucoma.　Most　efforts　focused　on　adopting　existing　techniques　to　the　SSVEPs-based　brain-computer　interface　(BCI)　task　rather　than　proposing　new　ones　specifically　suited　to　the　domain.　MethodGiven　that　electroencephalogram　(EEG)　signals　possess　temporal,　regional,　and　synchronous　characteristics　of　brain　activity,　we　proposed　a　transformer-based　EEG　analysis　model　known　as　EEGformer　to　capture　the　EEG　characteristics　in　a　unified　manner.　We　adopted　a　one-dimensional　convolution　neural　network　(1DCNN)　to　automatically　extract　EEG-channel-wise　features.　The　output　was　fed　into　the　EEGformer,　which　is　sequentially　constructed　using　three　components:　regional,　synchronous,　and　temporal　transformers.　In　addition　to　using　a　large　benchmark　database　(BETA)　toward　SSVEP-BCI　application　to　validate　model　performance,　we　compared　the　EEGformer　to　current　state-of-the-art　deep　learning　models　using　two　EEG　datasets,　which　are　obtained　from　our　previous　study:　SJTU　emotion　EEG　dataset　(SEED)　and　a　depressive　EEG　database　(DepEEG).　ResultsThe　experimental　results　show　that　the　EEGformer　achieves　the　best　classification　performance　across　the　three　EEG　datasets,　indicating　that　the　rationality　of　our　model　architecture　and　learning　EEG　characteristics　in　a　unified　manner　can　improve　model　classification　performance.　ConclusionEEGformer　generalizes　well　to　different　EEG　datasets,　demonstrating　our　approach　can　be　potentially　suitable　for　providing　accurate　brain　activity　classification　and　being　used　in　different　application　scenarios,　such　as　SSVEP-based　early　glaucoma　diagnosis,　emotion　recognition　and　depression　discrimination.