Towards a whole body brain-machine interface system for decoding expressive movement intent Challenges and Opportunities

摘要: The restoration and rehabilitation of human bipedal locomotion represent major goals for brain machine interfaces (BMIs), i.e., devices that translate neural activity into motor commands to control wearable robots enable locomotive non-locomotive tasks by individuals with gait disabilities. Prior BMI efforts based on scalp electroencephalography (EEG) have revealed fluctuations in the amplitude slow cortical potentials delta band contain information can be used infer intent, more specifically, kinematics walking such as sitting standing. However, little is known about extent which EEG discern expressive qualities influence functional movements. Here, we discuss how novel experimental approaches integrated learning techniques deployed decode movement. Applications artistic brain-computer (BCIs), movement aesthetics, neuroprostheses endowed are discussed.