Long-term　driving　is　a　significant　cause　of　fatigue-related　accidents.　Driving　mental　fatigue　has　major　implications　for　transportation　system　safety.　Monitoring　physiological　signal　while　driving　can　provide　the　possibility　to　detect　the　mental　fatigue　and　give　the　necessary　warning.　In　this　paper　an　EEG-based　fatigue　countermeasure　algorithm　is　presented　to　classify　the　driving　mental　fatigue.　The　features　of　multichannel　electroencephalographic　(EEG)　signals　of　frontal,　central　and　occipital　are　extracted　by　multivariate　autoregressive　(MVAR)　model.　Then　kernel　principal　component　analysis　(KPCA)　and　support　vector　machines　(SVM)　are　employed　to　identify　three-class　EEG-based　driving　mental　fatigue.　The　results　show　that　KPCA-SVM　method　is　able　to　effectively　reduce　the　dimensionality　of　the　feature　vectors,　speed　up　the　convergence　in　the　training　of　SVM　and　achieve　higher　recognition　accuracy　(81.64%)　of　three　driving　mental　fatigue　states　in　10　subjects.　The　KPCA-SVM　method　could　be　a　potential　tool　for　classification　of　driving　mental　fatigue.　(C)　2010　Elsevier　Ltd.　All　rights　reserved.