Labib S.S.Faculty of Computer ScienceOctober University for Modern Science and ArtsGizaEgypt2020-01-092020-01-0920169.78E+12https://doi.org/10.1109/ICDIPC.2016.7470787PubMed ID :https://t.ly/BJP2pScopusBrain computer interface (BCI) systems measure brain signal and translate it into control commands in an attempt to mimic specific human thinking activities. In recent years, many researchers have shown their interests in BCI systems, which has resulted in many experiments and applications. The main issue to build applicable Brain-Computer Interfaces is the capability to classify the Electroencephalograms (EEG). The purpose behind this research is to improve a model for brain signals analysis. We have used high pass filter to remove artifacts, discrete wavelet transform algorithms for feature extraction and statistical features like Mean Absolute Value, Root Mean Square, and Simple Square Integral are used, also we have used principle component analysis to reduce the size of feature vector and we used fuzzy Gaussian membership function to optimize the classification phase. It has been depicted from results that the proposed integrated techniques outperform a better performance than methods mentioned in literature. � 2016 IEEE.EnglishBrain Computer InterfaceEEGPrinciple Component AnalysisSupport Vector MachineWavelet TransformBioelectric phenomenaBiomedical signal processingBrainComputer control systemsDiscrete wavelet transformsElectroencephalographyFeature extractionFuzzy filtersFuzzy logicHigh pass filtersImage retrievalInformation scienceInterfaces (computer)Membership functionsPrincipal component analysisSupport vector machinesWavelet transformsControl commandDiscrete wavelet transform algorithmsElectro-encephalogram (EEG)Gaussian membership functionIntegrated techniquesPrinciple component analysisRoot Mean SquareStatistical featuresBrain computer interfaceConference Paperhttps://doi.org/10.1109/ICDIPC.2016.7470787PubMed ID :