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Classification of EEG-based Brain Waves for Motor Imagery using Support Vector Machine

Brain-computer interface (BCI) is a hardware and software communication system that allows controlling computers or external devices to utilize brain activity. BCI allows users to control computers or other devices using brain waves. The process of identifying patterns of brain activity depends on the classification algorithm. A portable BCI system classifiers need the ability to identify patterns obtained from electroencephalogram (EEG) channels. In this research, a reliable classification system was built using the Support Vector Machine (SVM) classification algorithm that are suitable for recognizing brain wave patterns. The SVM algorithm was implemented to identify five activity patterns from 4-channel EEG when performing different motor movements. The results show that SVM performance is reliable in recognizing and distinguishing those patterns based on the EEG’s gamma waves.