Five-class motor imagery BCI classification and its application to brain-controlled wheelchairs

Key Points

• The classification method achieves accurate differentiation among five motor imagery classes, enhancing user control.
• Accuracy rates exceed 85% for identifying motor imagery signals across various brain activities, showcasing effectiveness.
• Analysis of brain-computer interface frameworks facilitates improved communication between brain signals and wheelchair navigation.
• Future developments may enable easier access to brain-controlled mobility devices for users with physical disabilities.