A　new　nonlinear　approach　is　presented　for　high-frequency　electrocorticography　(ECoG)-based　diagnosis　of　epilepsy.　The　ECoG　data　from　3　patients　with　epilepsy　are　analyzed　in　this　study.　A　recently　developed　algorithm　in　graph　theory,　visibility　graph　(VG),　is　applied　in　this　research.　The　approach　is　based　on　the　key　discovery　that　high-frequency　oscillation　takes　place　during　epileptic　seizure,　making　it　a　marker　of　epilepsy.　Therefore,　the　nonlinear　property　of　the　high-frequency　signal　may　be　more　noticeable.　Hence,　a　complexity　measure,　called　graph　index　complexity　(GIC),　is　computed　using　the　VG　of　the　patients＇　high-frequency　ECoG　subband.　After　comparison　and　statistical　analysis,　the　nonlinear　feature　is　proved　to　be　effective　in　detection　and　location　of　the　epilepsy.　Two　different　traditional　complexities,　sample　entropy　and　Lempel-Ziv,　were　also　calculated　to　make　a　comparison　and　prove　that　GIC　provides　better　identification.