The　progress　of　EEG-based　emotion　recognition　has　received　widespread　attention　from　the　fields　of　human-machine　interaction　and　cognitive　science.　However,　recognizing　emotions　with　limited　labelled　data　is　still　challenging.　To　address　this　issue,　this　paper　proposes　a　self-supervised　group　meiosis　contrastive　learning　(SGMC)　framework　for　EEG-based　emotion　recognition.　First,　to　reduce　the　dependence　of　emotion　labels,　SGMC　introduces　a　contrastive　learning　task　according　to　the　alignment　of　video　clips　based　on　the　similar　EEG　response　across　subjects.　Moreover,　the　model　adopts　a　group　projector　to　extract　group-level　representations　from　the　group　samples　to　further　decrease　the　subject　difference　and　random　effects　in　EEG　signals.　Finally,　a　novel　genetics-inspired　data　augmentation　method,　named　meiosis　is　developed,　which　takes　advantage　of　the　alignment　of　video　clips　among　a　group　of　EEG　samples　to　generate　augmented　groups　by　pairing,　cross　exchanging,　and　separating.　The　experiments　show　that　SGMC　exhibits　competitive　performance　on　the　publicly　available　DEAP　and　SEED　datasets.　It　is　worth　of　noting　that　the　SGMC　shows　a　high　ability　to　recognize　emotion　even　when　using　limited　labelled　data.　Moreover,　the　results　of　feature　visualization　suggest　that　the　model　might　have　learned　video-level　emotion-related　feature　representations　to　improve　emotion　recognition.　The　hyper-parametric　analysis　further　shows　the　effect　of　the　group　size　during　emotion　recognition.　Finally,　the　comparisons　of　both　the　symmetric　function　and　the　ablation　models　and　the　analysis　of　computational　efficiency　are　carried　out　to　examine　the　rationality　of　the　SGMC　architecture.　The　code　is　provided　publicly　online.　©　2023,　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.