Event-relatedpotentials(ERPs)arecommonlyusedinEEGresearchtostudybrain responsestospecificstimuli.TraditionalERPaveragingmethodsareaffectedbytrial-to- trial latencyvariability,whichcandistorttheextractedcomponents.ResidualIteration DecompositionEstimate(RIDE)isanestablishedmethoddesignedtocorrectforthis variabilitybyseparatingcomponentsbasedontheirlatencydistributions.Whileeffective, its originalMATLABimplementationlacksflexibilityandintegrationwithmodernEEG analysis frameworks. This workpresentsaJulia-basedimplementationofRIDEanditsintegrationintoUn- fold, aregression-baseddeconvolutiontoolboxforEEGanalysis.Twoversionswerede- veloped:ClassicRIDE,whichreplicatesMATLABRIDE’sbehavior,andUnfoldRIDE, whichreplacesiterativedecompositionwithregression-baseddeconvolution.Bothim- plementationswereevaluatedusingsimulateddatasetswithknowngroundtruthanda real-worldEEGdatasettoassesstheiraccuracyandcomparethemwithMATLABRIDE. The resultsshowthatClassicandUnfoldRIDEsuccessfullyreconstructERPcom- ponents,producingreasonableresultsinbothdatasets.However,differencesexistwhen compared toMATLABRIDE,likelyduetomissingfeaturesandvariationsinfiltering,la- tency estimation,anditerationbehavior.Despitethesediscrepancies,thefindingsconfirm that Unfold’sregression-basedapproachisaviablealternativetoiterativedecomposition, offering potentialadvantagessuchasimprovedmodelingflexibility. Futureresearchshouldfocusonimplementingmissingfeaturesandachievingequiv- alence withMATLABRIDE.Additionally,randomizedtestingwithUnfoldSimcould providesystematicvalidationandhelpoptimizethealgorithm.Thisworklaysthefoun- dation forfurtherimprovementsinEEGdeconvolutionbycombiningRIDE’slatency estimation withUnfold’sflexibleregressionframework. The implementedalgorithmsareavailableasanopensourceprojectonGitHub: https://github.com/unfoldtoolbox/UnfoldRIDE.jl