Trans Humeral Prosthesis Based on sEMG and SSVEPEEG Signals * The loss of forearm muscle in amputees above elbow joint make it impossible to control the prosthesis of elbow joint and upper limb only by using surface electromyography (sEMG) signals. Electroencephalogram (EEG) signals can be used as input signal to control the motion of the upper limb prosthetic hand for it can reflect the useru0027s motion intention. This paper introduces a method of controlling the trans humeral prosthesis by combining sEMG and EEG signals. In this method, the control of elbow flexion and extension motions are based on sEMG signals of biceps and triceps. Combined with the collected elbow angles, the elbow angle of prosthetic arm is predicted by back propagation neural network after training and then the angle can be used to control the elbow joint. In order to control the motion of the prosthetic hand, a control method based on EEG is proposed. The EEG control method is named as steady state visual evoked potential (SSVEP). User can use his EEG signals to control the motion of hand prosthesis. Canonical correlation analysis (CCA) algorithm is used to classify SSVEP signals, then different SSVEP signals can be used to control different motions of prosthetic hands. Some experiments were carried out on healthy subjects to verify the performance of the proposed system.

A LowCost Arm Robotic Platform based on Myoelectric Control for Rehabilitation Engineering Rehabilitation robotics is a recent kind of service robot that include devices such as robotic prosthesis and exoskeletons. These devices could help motor disabled people to rehabilitate their motor functions, and could provide functional compensation to accomplish motor activities. In order to control robotic prosthesis and exoskeletons it is required to identify human movement intention, to be converted into commands for the device. Motor impaired people may use surface electromyography (sEMG) signals to control these devices, taking into account that sEMG signals directly reflects the human motion intention. Myoelectric control is an advanced technique related with the detection, processing, classification, and application of sEMG signals to control humanassisting robots or rehabilitation devices. Despite recent advances with myoelectric control algorithms, currently there is still an important need to develop suitable methods involving usability, for controlling prosthesis and exoskeletons in a natural way. Traditionally, acquiring EMG signals and developing myoelectric control algorithms require expensive hardware. With the advent of lowcost technologies (i.e. sensors, actuators, controllers) and hardware support of simulation software packages as Matlab, affordable research tools could be used to develop novel myoelectric control algorithms. This work describes the implementation and validation of a Matlabbased robotic arm using lowcost technologies such as Arduino commanded using myoelectric control. The platform permits implementation of a variety of EMGbased algorithms. It was carried out a set of experiments aimed to evaluate the platform, through an application of pattern recognition based myoelectric control to identify and execute seven movements of the robotic upper limb: 1forearm pronation; 2 forearm supination; 3wrist flexion; 4wrist extension; 5 elbow flexion; 6 elbow extension; 7resting. The algorithm use a feature extraction stage based on a combination of time and frequency domain features (mean absolute value, waveform length, root mean square) and a widely used kNN classifier. Obtained mean classification errors were 5.9%. As future work, additional features in the myoelectric control algorithm will be evaluated, for realtime applications.