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Electroencephalography (EEG) is almost contaminated with many artifacts while recording the brain signal activity. Clinical diagnostic and brain computer interface applications frequently require the automated removal of artifacts. In digital signal processing and visual assessment, EEG artifact removal is considered to be the key analysis technique. Nowadays, a standard method of dimensionality reduction technique like independent component analysis (ICA) and wavelet transform combination can be explored for removing the EEG signal artifacts. Manual artifact removal is time-consuming; in order to avoid this, a novel method of wavelet ICA (WICA) using fuzzy kernel support vector machine (FKSVM) is proposed for removing and classifying the EEG artifacts automatically. Proposed method presents an efficient and robust system to adopt the robotic classification and artifact computation from EEG signal without …