Yesilkaya B.Guren O.Bahar M.T.Turhal L.N.Akan A.2023-06-162023-06-1620209.78E+12https://doi.org/10.1109/SIU49456.2020.9302223https://hdl.handle.net/20.500.14365/361728th Signal Processing and Communications Applications Conference, SIU 2020 -- 5 October 2020 through 7 October 2020 -- 166413Emotion recognition is an effective analysis method used to increase the interaction between human-machine interface. EEG based emotion recognition studies based on brain signals are preferred in order to provide healthy results of emotion analysis experiments. In this study, emotion recognition analysis was performed in accordance with dimensional emotion modelling. Data cleaning was performed by applying the necessary filters on the recorded data. The feature vector was then created and the success rate was determined using support vector machines and classification methods such as K-nearest neighbour. © 2020 IEEE.trinfo:eu-repo/semantics/closedAccessEGEEmotionEmotion RecognitionEnsemble ClassifierFeature VectorInternational Affective Image SystemK-nearest neighbourSupport vector machinesNearest neighbor searchSpeech recognitionSupport vector machinesClassification methodsEffective analysisEmotion analysisEmotion modellingEmotion recognitionFeature vectorsHuman Machine InterfaceK-nearest neighboursSignal processingConference Object10.1109/SIU49456.2020.93022232-s2.0-85100311182