Purpose - The purpose of this paper is to tune support vector machine (SVM) classifier using grey Wolf optimizer (GWO). Design/methodology/approach - The schema of the work aims at extracting the features from the collected data followed by a SVM classifier and metaheuristic optimization to tune the classifier parameters. Findings - The optimal tuning of classifier parameters lowers errors due to manual elucidation and decreases the risk in human perceptions and repeated visual dignosis. Originality/value -A novel, GWO based tuning algorithm is used for SVM classifier, which is implemented in classifying the complex and nonlinear biomedical signals like intracranial electroencephalogram. © Emerald Group Publishing Limited.