The　theory　of　feature　extraction　and　pattern　classification　of　Motion　Imaging　Electroencephalogram　(MI-EEG)　plays　an　important　role　in　Brain-computer　Interface　(BCI)　system,　which　is　widely　used　in　rehabilitation　medicine,　intelligent　control　and　other　fields.　Although　advanced　acquisition　technologies　have　generated　considerable　EEG　data　for　various　brain　areas,　it　has　inevitable　drawbacks　such　as　high　cost,　time　consumption,　and　inherently　high　false　positive　rate　of　existing　methods.　In　this　paper,　considering　the　contradiction　between　data　quantity　and　accuracy,　we　used　a　two-step　feature　extraction　method　based　on　Discrete　Cosine　Transform　(DCT)　together　with　Least　Squares　Support　Vector　Machine　(LS-SVM)　to　perform　feature　extraction　and　classification　for　single-Trial　MI　EEG.　Based　on　the　dataset　constructed　from　the　Project　BCI-EEG　motor　activity,　the　average　ACC,　PE,　SN　and　MCC　of　seven　channels　in　the　central　and　temporal　lobes　of　the　brain　are　90.27%,　91.28%,　89.35%,　and　82.34%　were　obtained　with　5-fold　cross　validation,　respectively.　Furthermore,　the　same　method　was　applied　to　the　BCI　Competition　IV　dataset,　and　the　above　results　were　also　confirmed.　The　performance　comparison　with　previous　prediction　models　show　that　our　method　used　fewer　channels,　but　the　accuracy　was　higher　and　more　reliable.　It　is　anticipated　that　the　proposed　method　can　be　used　as　an　effective　computational　tool　for　future　BCI　researches.　©　2022　IEEE.