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Adapting EEG based MI-BMI depending on alertness level for controlling a lower-limb exoskeleton

Motor imagery (MI) has been used as a control paradigm of external devices for brain-machine interfaces (BMI). One of the main limitations of EEG based MI-BMI systems is the sensitivity of the performance to changes in the environment, signal properties, and subjects’ state of mind, especially the ability to stay focused and their alertness. This paper presents a new approach for improving the accuracy of a MI-BMI for controlling a lower-limb exoskeleton by adapting the classifier depending on the level of alertness. Results showed that the performance of MI-BMI sightly improves when predictions are modified based on alertness level, especially for subjects that had lower baseline accuracy.