In　this　paper,　an　intelligent　and　low　power　EEG(electroencephalograph)　processing　multi-QOS(quality　of　service)　DSP　has　been　designed　with　the　smicrf180nm　technology　used　for　EEG　wearable　Instrument.　The　bio-detection　uses　the　Ag/Ag-Cl　electrode　sensor　to　extract　the　head　skin＇s　micro　EEG　signal,　and　uses　the　differential　chopper-LNA　circuit　to　cancel　the　1/f,　dc-offset　voltage　and　other　noises.　On　one　hand,　the　hardware　accelerator　uses　the　STFT(short　time　fourier　transform)　multiple　filtering　channels　and　the　HT(hilbert　transform)　method,　for　decomposing　the　instant　amplitude　envelop　and　instant　frequency　of　each　sub-channel.　On　the　other　hand,　when　the　detected　SI(sleepiness　indicator)　signal　triggers　the　minimum　3-stage　CPU,　the　software　thread　uses　the　HHT(hilbert　huang　transform)　method　to　get　the　IMF(intrinsic　mode　function)　components　from　the　non-statistical　and　non-stable　EEG　signal,　then　the　design　executes　the　Tsallis　entropy　complexity　analysis　with　the　pipeline　CPU,　which　consumes　about　20pJ/instruction.　The　transmitter　uses　ping/pang　switching　matrix,　the　burst　TD-OFDM　(time　division-orthogonal　frequency　division　multiplex)　scheme　and　the　power-adaptation　algorithm　to　give　a　balance　design　between　the　frequency　utility　and　the　energy　consumption.　To　increase　the　throughput　and　to　decrease　power　consumption　mostly　caused　by　the　wireless　channel＇s　idle　CS　(carrier　sensing)　operation,　the　channel　wake-upping/monitoring　and　the　data　transmitting/receiving　circuits　are　separated,　which　reduces　the　energy　consumption　by　about　30×.　The　design　uses　one-hop　star　topology　to　compose　the　reduced　network　architecture.　Also,　the　design　mainly　uses　the　CG　(clock　gating)　and　PG　(power　gating)　methods　to　decrease　the　chip＇s　dynamic　and　leakage　energy　consumption　respectively.　©　2016　IEEE.