In　brain　computer　interface-based　neurorehabilitation　system,　a　large　number　of　electrodes　may　increase　the　difficulty　of　signal　acquisition　and　the　time　consumption　of　decoding　algorithm　for　motor　imagery　EEG　(MI-EEG).　The　traditional　electrode　optimization　methods　were　limited　by　the　low　spatial　resolution　of　scalp　EEG.　EEG　source　imaging　(ESI)　was　further　applied　to　reduce　the　number　of　electrodes,　in　which　either　the　electrodes　covering　activated　cortical　areas　were　selected,　or　the　reconstructed　electrodes　of　EEGs　with　higher　Fisher　scores　were　retained.　However,　the　activated　dipoles　do　not　all　contribute　equally　to　decoding,　and　the　Fisher　score　cannot　represent　the　correlations　between　electrodes　and　dipoles.　In　this　paper,　based　on　ESI　and　correlation　analysis,　a　novel　electrode　optimization　method,　denoted　ECCEO,　was　developed.　The　scalp　MI-EEG　was　mapped　to　cortical　regions　by　ESI,　and　the　dipoles　with　larger　amplitudes　were　chosen　to　designate　a　region　of　interest　(ROI).　Then,　Pearson　correlation　coefficients　between　each　dipole　of　the　ROI　and　the　corresponding　electrode　were　calculated,　averaged,　and　ranked　to　obtain　two　average　correlation　coefficient　sequences.　A　small　but　important　group　of　electrodes　for　each　class　were　alternately　added　to　the　predetermined　basic　electrode　set　to　form　a　candidate　electrode　set.　Their　features　were　extracted　and　evaluated　to　determine　the　optimal　electrode　set.　Experiments　were　conducted　on　two　public　datasets,　the　average　decoding　accuracies　achieved　95.99%　and　88.30%,　and　the　reduction　of　computational　cost　were　65%　and　56%,　respectively;　statistical　significance　was　examined　as　well.