title: Multisession, noninvasive closed-loop neuroprosthetic control of grasping by upper limb amputees.
abstract: Upper limb amputation results in a severe reduction in the quality of life of affected individuals due to their inability to easily perform activities of daily living. Brain-machine interfaces (BMIs) that translate grasping intent from the brain's neural activity into prosthetic control may increase the level of natural control currently available in myoelectric prostheses. Current BMI techniques demonstrate accurate arm position and single degree-of-freedom grasp control but are invasive and require daily recalibration. In this study we tested if transradial amputees (A1 and A2) could control grasp preshaping in a prosthetic device using a noninvasive electroencephalography (EEG)-based closed-loop BMI system. Participants attempted to grasp presented objects by controlling two grasping synergies, in 12 sessions performed over 5 weeks. Prior to closed-loop control, the first six sessions included a decoder calibration phase using action observation by the participants; thereafter, the decoder was fixed to examine neuroprosthetic performance in the absence of decoder recalibration. Ability of participants to control the prosthetic was measured by the success rate of grasping; ie, the percentage of trials within a session in which presented objects were successfully grasped. Participant A1 maintained a steady success rate (63±3%) across sessions (significantly above chance [41±5%] for 11 sessions). Participant A2, who was under the influence of pharmacological treatment for depression, hormone imbalance, pain management (for phantom pain as well as shoulder joint inflammation), and drug dependence, achieved a success rate of 32±2% across sessions (significantly above chance [27±5%] in only two sessions). EEG signal quality was stable across sessions, but the decoders created during the first six sessions showed variation, indicating EEG features relevant to decoding at a smaller timescale (100ms) may not be stable. Overall, our results show that (a) an EEG-based BMI for grasping is a feasible strategy for further investigation of prosthetic control by amputees, and (b) factors that may affect brain activity such as medication need further examination to improve accuracy and stability of BMI performance.
authors: Noninvasive Brain-Machine Interface Systems Lab, University of Houston, Houston, TX, United States. Electronic address: hagashe@uh.edu.
authors: H A
authors: HA
authors: Agashe
authors: Noninvasive Brain-Machine Interface Systems Lab, University of Houston, Houston, TX, United States.
authors: A Y
authors: AY
authors: Paek
authors: Noninvasive Brain-Machine Interface Systems Lab, University of Houston, Houston, TX, United States.
authors: J L
authors: JL
authors: Contreras-Vidal
mesh_terms: D000368:Aged; D000674:Amputees / Q000534:rehabilitation*; D001186:Artificial Limbs; D001931:Brain Mapping; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D005260:Female; D005500:Follow-Up Studies; D018737:Hand Strength / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D012815:Signal Processing, Computer-Assisted; D034941:Upper Extremity / Q000502:physiology*
keywords: Amputee; Brain–machine interfaces; Electroencephalography; Grasping