title: Channel and feature selection in multifunction myoelectric control.
abstract: Real time controlling devices based on myoelectric singles (MES) is one of the challenging research problems. This paper presents a new approach to reduce the computational cost of real time systems driven by Myoelectric signals (MES) (a.k.a Electromyography--EMG). The new approach evaluates the significance of feature/channel selection on MES pattern recognition. Particle Swarm Optimization (PSO), an evolutionary computational technique, is employed to search the feature/channel space for important subsets. These important subsets will be evaluated using a multilayer perceptron trained with back propagation neural network (BPNN). Practical results acquired from tests done on six subjects' datasets of MES signals measured in a noninvasive manner using surface electrodes are presented. It is proved that minimum error rates can be achieved by considering the correct combination of features/channels, thus providing a feasible system for practical implementation purpose for rehabilitation of patients.
authors.affiliation: Mechatronics and Intelligent Systems Group, Faculty of Engineering, University of Technology, Sydney, Broadway NSW 2007, Australia. Rkhushab@eng.uts.edu.au
authors.forename: Rami N
authors.initials: RN
authors.lastname: Khushaba
authors.forename: Adel
authors.initials: A
authors.lastname: Al-Jumaily
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D001185:Artificial Intelligence; D004576:Electromyography / Q000295:instrumentation / Q000379:methods*; D019544:Equipment Failure Analysis; D005246:Feedback; D006801:Humans; D009119:Muscle Contraction / Q000502:physiology; D018482:Muscle, Skeletal / Q000294:innervation / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011474:Prosthesis Design; D013813:Therapy, Computer-Assisted / Q000379:methods*