Smooth coordination and fine temporal control of muscles by the brain allows us to effortlessly pre-shape our hand to grasp different objects. Correlates of motor control for grasping have been found across wide-spread cortical areas, with diverse signal features. These signals have been harnessed by implanting intracortical electrodes and used to control the motion of robotic hands by tetraplegics, using algorithms called brain-machine interfaces (BMIs). Signatures of motor control signal encoding mechanisms of the brain in macro-scale signals such as electroencephalography (EEG) are unknown, and could potentially be used to develop noninvasive brain-machine interfaces. Here we show that a) low frequency (0.1 – 1 Hz) time domain EEG contains information about grasp pre-shaping in able-bodies individuals, and b) This information can be used to control pre-shaping motion of a robotic hand by amputees. In the first study, we recorded simultaneous EEG and hand kinematics as 5 able-bodies individuals grasped various objects. Linear decoders using low delta band EEG amplitudes accurately predicted hand pre-shaping kinematics during grasping. Correlation coefficient between predicted and actual kinematics was r = 0.59 ± 0.04, 0.47 ± 0.06 and 0.32 ± 0.05 for the first 3 synergies. In the second study, two transradial amputees (A1 and A2) controlled a prosthetic hand to grasp two objects using a closed-loop BMI with low delta band EEG. This study was conducted longitudinally in 12 sessions spread over 38 days. A1 achieved a 63% success rate, with 11 sessions significantly above chance. A2 achieved a 32% success rate, with 2 sessions significantly above chance. Previous methods of EEG-based BMIs used frequency domain features, and were thought to have a low signal-to-noise ratio making them unsuitable for control of dexterous tasks like grasping. Our results demonstrate that time-domain EEG contains information about grasp pre-shaping, which can be harnessed for neuroprosthetic control. ; Electrical and Computer Engineering, Department of


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Noninvasive Neuroprosthetic Control of Grasping by Amputees



    Erscheinungsdatum :

    2014-12-01


    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    A review of control strategies in closed-loop neuroprosthetic systems

    Wright, James (S31674) / Macefield, Vaughan G. (R11708) / Schaik, Andre van (R16638) et al. | BASE | 2016

    Freier Zugriff

    Toward building hybrid biological/in silico neural networks for motor neuroprosthetic control

    Kocatürk, Mehmet / Gülcür, Halil Özcan / Canbeyli, Reşit | BASE | 2015

    Freier Zugriff

    Bicycle modifications for amputees

    Harp, A.J. | Tema Archiv | 1997


    Grasping method by grasping apparatus

    MATSUOKA HIROFUMI | Europäisches Patentamt | 2016

    Freier Zugriff

    GRASPING MECHANISM AND GRASPING DEVICE

    KABASHIMA TAKEFUMI / NISHIDA TAKESHI / SAKAMOTO NAOHITO et al. | Europäisches Patentamt | 2019

    Freier Zugriff