EEG-based Brain-Machine Interaction
Understanding the EEG-based Brain-Machine Interaction Project
The control of machines and robots, including wearable augmentation devices using brain signals, holds great promise for enhancing the quality of life for both able-bodied and disabled individuals. This technology has applications in two key areas: restorative augmentation, which aims to regain lost abilities, and enhancement augmentation, which seeks to extend natural capabilities. This research strives to develop systems and methods to generate and decode electroencephalography (EEG) brain signals associated with motor imagery functions using machine learning to achieve reliable and personalized control.
Key factors in improving the controllability of wearable augmentations are improved motor imagery and providing sensory feedback. The first is achieved by employing motor imagery training techniques, such as motor observation, priming, and imagery using extended reality (XR) technologies, while the latter is achieved through conveying sensory proprioceptive and tactile feedback pertaining to the movement of the wearable augmentation. Furthermore, EMG will be considered to improve the control of wearable augmentation.
The figure below is a pictorial schematic showing the interaction between the control elements (EEG/EMG), sensory feedback, and the long-term impact of using the wearable augmentation, represented by neuroplasticity.
