2017 · 30 citations · 13 references
EngineeringBiomedical EngineeringElectrical EngineeringNeural EngineeringProstheticsRehabilitation EngineeringBrain-computer InterfacesElectroencephalogram Signal ProcessingElectroencephalographyNeural ProstheticsProsthetic DevicesUpper-limb ProstheticsBrain-computer Interface Signal AcquisitionElectromyography-based Prosthetic ControlNeural Interface ProstheticsProsthetic Arm SystemsProsthetic ArmsProsthetic ControlProsthetic Limb ControlUpper Limb Prosthetic Rehabilitation
Lack of forearm muscles in transhumerai amputees is one of the major issues when it comes to control of upper-limb prosthetic arms using only EMG signals. Brain signal is one of the alternative input signals that can be explored to recognize the motion intention of the users and subsequently can be used to control the upper-limb prosthetic hands. This paper proposes a transhumeral prosthetic arm which is controlled using both EMG and EEG signals. The proposed transhumeral prosthetic arm consists of 1 DOF elbow joint and 6 DOF prosthetic hand called UOMPro. In the proposed system, the elbow joint flexion/extension motions are realized based on EMG signals of biceps and triceps muscles. A real time fuzzy-neuro network based method is used for controlling the elbow joint of the prosthetic arm using EMG signals. In order to control the hand open/close motions of the hand prosthetic, EEG-signal based approach is proposed. The proposed EEG-based approach is based on Steady State Visual Evoked Potentials (SSVEP) where user can use his/her brain signals to open/close the hand prosthetic. A simple yet effective threshold based algorithm is used recognize the SSVEP signal levels and results are mapped in to a low-level controller of the hand prosthetic. A set of experiments are conducted with healthy subjects to validate the performance of the proposed system and results are presented with potential future directions.
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