A　novel　method　based　on　machine　learning　is　developed　to　estimate　event-related　potentials　from　single　trial　electroencephalography.　This　paper　builds　a　basic　framework　using　classification　and　an　optimization　model　based　on　this　framework　for　estimating　event-related　potentials.　Then　the　SingleTrialEM　algorithm　is　derived　by　introducing　a　logistic　regression　model,　which　could　be　obtained　by　training　before　SingleTrialEM　is　used,　to　instantiate　the　optimization　model.　The　simulation　tests　demonstrate　that　the　proposed　method　is　correct　and　solid.　The　advantage　of　this　method　is　verified　by　the　comparison　between　this　method　and　the　Woody　filter　in　simulation　tests.　Also,　the　cognitive　test　results　are　consistent　with　the　conclusions　of　cognitive　science.