Brain-computer interfaces (BCIs) can be used to control neuroprostheses of spinal cord injured
(SCI) persons. A neuroprosthesis can restore different movement functions (e.g., hand open/close,
supination/pronation etc.), and requires a BCI with a sufficiently high number of classes. However, sensorimotor rhythm-based BCIs can often only provide less than 3 classes, and new types of BCIs need to be developed. Since a couple of years, a new EEG feature has evolved: low-frequency time-domain signals. For example movement trajectories [1] and movement directions [2] were decoded using this feature. In the present study, we investigated whether low-frequency time-domain signals can also be used to classify several (executed) hand/arm movements of the same limb. A BCI relying on the imagination of such movements may be used to control a neuroprosthesis more naturally and provide a higher number of classes
