Blind source separation based classification scheme for myoelectric prosthesis hand

For over three decades, researchers have been working on using surface electromyography (sEMG) as a means for amputees to use remaining muscles to control prosthetic limbs (Baker, Scheme, Englehart, Hutcinson, & Greger, 2010; Hamdi, Dweiri, Al-Abdallat, & Haneya, 2010; Kiguchi, Tanaka, & Fukuda, 2004). Most research in this domain has focused on using the muscles of the upper arms and shoulders to control the gross orientation and grasp of a low-degree-of-freedom prosthetic device for manipulating objects (Jacobsen & Jerard, 1974). Each measured upper arm muscle is typically mapped directly to one degree of freedom of the prosthetic. For example, tricep contraction could be used for rotation while bicep flexion might close or open the prosthetic. More recently, researchers have begun to look at the potential of using the forearm muscles in hand amputees to control a multi-fingered prosthetic hand. While we know of no fully functional hand prosthetic, this is clearly a promising new area of EMG research. One of the challenges for creating hand prosthetics is that there is not a trivial mapping of individual muscles to finger movements. Instead, many of the same muscles are used for several different fingers (Schieber, 1995).