Deep　convolutional　neural　network　(DCNN)　has　been　successfully　applied　to　improve　the　classification　performance　of　motor　imagery　(MI)　tasks　in　the　electroencephalogram　(EEG)　based　brain　computer　interface　(BCI).　However,　there　is　still　a　great　challenge　about　how　to　extract　effective　features　from　EEG　signals.　So,　a　novel　parallel　DCNN　structure-based　MI-EEG　classification　method　is　proposed　in　this　paper.　Fast　Fourier　transform　is　utilized　to　transform　EEG　signals　from　time　domain　to　frequency　domain.　Then,　μ　and　β　rhythms,　which　are　related　to　MI　tasks,　are　redivided　into　three　sub-bands.　The　averaged　power　is　calculated　for　each　sub-band.　In　order　to　utilize　the　position　information　of　electrodes,　azimuthal　equidistant　projection　is　adopted　to　convert　the　3-D　location　of　each　electrode　into　2-D　position.　Then,　Clough-Tocher　interpolation　algorithm　is　employed　to　generate　a　spatio-frequency　image　for　each　sub-band.　Furthermore,　a　parallel　DCNN　(pDCNN),　which　includes　three　DCNNs　correspondings　to　three　sub-bands　respectively,　is　designed　to　extract　and　classify　spatio-frequency　features.　The　BCI2000　dataset　is　adopted,　and　the　average　accuracy　and　Kappa　value　reach　90.25%　and　0.81　respectively,　which　are　greater　than　that　of　the　existing　methods.　These　experimental　results　show　that　the　effectiveness　of　the　proposed　MI-EEG　classification　method.　©　2021　IEEE