First study towards linear control of an upper-limb neuroprosthesis with an EEG-based Brain-Computer Interface.
In this study we show how healthy subjects are able to use a non-invasive Motor Imagery (MI)-based Brain Computer Interface (BCI) to achieve linear control of an upper-limb neuromuscular electrical stimulation (NMES) controlled neuroprosthesis in a simple binary target selection task. Linear BCI control can be achieved if two motor imagery classes can be discriminated with a reliability over 80% in single trial. The results presented in this work show that there was no significant loss of performance using the neuroprosthesis in comparison to MI where no stimulation was present. However, it is remarkable how different the experience of the users was in the same experiment. The stimulation either provoked a positive reinforcement feedback, or prevented the user from concentrating in the task.
Machine Learning Group, Computer Science Faculty, Berlin Institute of Technology, Berlin, Germany.
Javier
J
Pascual
Francisco
F
Velasco-Alvarez
Klaus-Robert
KR
Muller
Carmen
C
Vidaurre
D001132:Arm / Q000502:physiology*; D001676:Biofeedback, Psychology; D062207:Brain-Computer Interfaces; D004558:Electric Stimulation; D004569:Electroencephalography / Q000295:instrumentation*; D006801:Humans; D019736:Prostheses and Implants