In  this  software  demonstration,  we  will  show   how  formal  ontologies  can  be  used  to  label  in-­stances   of   neural   (ERP)   patterns   that   have   been   extracted   from   multiple   datasets   using   a   novel   pipeline   for   pattern   and   metric   extrac-­tion  (see  Figure  1,  next  page).  The  entire  dem-­onstration  will  last  ~15  minutes.  We  will  begin   with  a  5-­‐minute  introduction  to  ERP  data  from   several   cross-­‐laboratory   studies   of   word   com-­prehension.   This   overview   will   motivate   our   demonstration   by   showing   that   ERP   data   are   complex   and   heterogeneous,   which   explains   the   radical   challenge   of   making   valid   compari-­sons   across   different   studies   within   our   do-­main.  We  will  then  give  a  2-­‐minute  description   of  the  pipeline  for  analysis,  which  has  two  main   components:   (1)   a   set   of   pattern   extraction   (signal  decomposition,  temporal  segmentation)   methods;   and   (2)   code   to   extract   a   variety   of   simple  metrics  (e.g.,  min  and  max  intensity  at  a   particular   electrode)   and   to   express   these   summary   features   as   N-­‐triples,   which   are   sub-­sequently   stored   in   RDF.   Finally,   we   demon-­strate   how   the   NEMO   ontology   can   be   used   to   reason   over   these   data.   We   highlight   both   ex-­pected   and   novel   findings   for   the   test   datasets   and   note   that   large-­‐scale   application   of   this   method   could   lead   to   major   breakthroughs   in   understanding   neurological   patterns   that   are   linked   to   sensory,   motor,   and   cognitive   pro-­cesses   in   neurologically   healthy   and   brain-­injured  children  and  adults.