The　health-related　Internet　of　Things　(IoT)　plays　an　irreplaceable　role　in　the　collection,　analysis,　and　transmission　of　medical　data.　As　a　device　of　the　health-related　IoT,　the　electroencephalogram　(EEG)　has　long　been　a　powerful　tool　for　physiological　and　clinical　brain　research,　which　contains　a　wealth　of　personal　information.　Due　to　its　rich　computational/storage　resources,　cloud　computing　is　a　promising　solution　to　extract　the　sophisticated　feature　of　massive　EEG　signals　in　the　age　of　big　data.　However,　it　needs　to　solve　both　response　latency　and　privacy　leakage.　To　reduce　latency　between　users　and　servers　while　ensuring　data　privacy,　we　propose　a　privacy-preserving　feature　extraction　scheme,　called　LightPyFE,　for　EEG　signals　in　the　edge　computing　environment.　In　this　scheme,　we　design　an　outsourced　computing　toolkit,　which　allows　the　users　to　achieve　a　series　of　secure　integer　and　floating-point　computing　operations.　During　the　implementation,　LightPyFE　can　ensure　that　the　users　just　perform　the　encryption　and　decryption　operations,　where　all　computing　tasks　are　outsourced　to　edge　servers　for　specific　processing.　Theoretical　analysis　and　experimental　results　have　demonstrated　that　our　scheme　can　successfully　achieve　privacy-preserving　feature　extraction　for　EEG　signals,　and　is　practical　yet　effective.