Background:　Traditional　Common　Spatial　Pattern　(CSP)　algorithms　for　Electroencephalogram　(EEG)　signal　classification　are　sensitive　to　noise　and　can　produce　low　accuracy　in　small　sample　datasets.　New　method:　To　solve　the　problem,　an　improved　Empirical　Mode　Decomposition　(EMD)　Bagging　Regularized　CSP　(RCSP)　algorithm　is　proposed.　It　filters　EEG　signals　through　improved　EMD,　inhibits　high-frequency　noise,　retains　effective　information　in　the　characteristic　frequency　band,　and　uses　Bagging　algorithm　for　data　reconstruction.　Feature　extraction　is　performed　with　regularization　of　spatial　patterns　and　Fisher　linear　discriminant　analysis　for　feature　classification.　T-test　is　used　for　classification.　Results:　The　improved　EMD　Bagging　RCSP　algorithm　has　improved　accuracy　and　robustness　compared　to　CSP　and　its　derivatives.　The　average　classification　rate　is　increased　by　about　6%,　demonstrating　the　effectiveness　and　correctness　of　the　proposed　algorithm.　Comparison　with　existing　methods:　The　proposed　algorithm　outperforms　CSP　and　its　derivatives　by　retaining　effective　information　and　inhibiting　high-frequency　noise　in　small　sample　EEG　datasets.　Conclusions:　The　proposed　EMD　Bagging　RCSP　algorithm　provides　a　reliable　and　effective　method　for　EEG　signal　classification　and　can　be　used　in　various　applications,　including　brain-computer　interfaces　and　clinical　EEG　diagnosis.　©　2024