Wavelet Transform Based Feature Extraction for EEG Signal Classification
Seda Postalcıoğlu
Abstract
This study focused on the classification of EEG signal. The study aims to make a classification with fast response and high-performance rate. Thus, it could be possible for real-time control applications as Brain-Computer Interface (BCI) systems. The feature vector is created by Wavelet transform and statistical calculations. It is trained and tested with a neural network. The db4 wavelet is used in the study. Pwelch, skewness, kurtosis, band power, median, standard deviation, min, max, energy, entropy are used to make the wavelet coefficients meaningful. The performance is achieved as 99.414% with the running time of 0.0209 seconds
Topics & Concepts
KurtosisPattern recognition (psychology)SkewnessWaveletFeature extractionArtificial intelligenceComputer scienceWavelet transformSupport vector machineBrain–computer interfaceApproximate entropyStandard deviationSpeech recognitionElectroencephalographySIGNAL (programming language)Entropy (arrow of time)Feature (linguistics)Discrete wavelet transformMathematicsStatisticsLinguisticsQuantum mechanicsPsychologyPhysicsProgramming languagePsychiatryPhilosophyEEG and Brain-Computer InterfacesBlind Source Separation TechniquesNeural Networks and Applications