作者: T. Khanam , S. Siuly , H. Wang
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摘要: Motor disability affects a person's ability to move and maintain balance. To remove this pain from the society, brain computer interface (BCI) system with the assistance of motor imagery (MI) tasks classification plays an important role. BCI translates human intension by brain activity into control signals to communicate with their external environment without direct physical movement. The current BCI system works with massive data through electroencephalogram (EEG) signals. Traditional methods in BCI are limited in efficiency, accuracy, and speed. To overcome these limitations, this study aims to develop an optimized artificial Intelligence-based technique for identifying human intentions of physical movement through EEG data for an advanced BCI system. The proposed technique is designed involving Common Spatial Pattern (CSP) and Medium K Nearest Neighbour (MKNN) technique to achieve higher classifier …