Data Driven Spatial Filtering Can Enhance Abstract Myoelectric Control in Amputees

Myoelectric control based on multi-sensor techniques can provide an enhanced signal to noise ratio but increases hardware cost and complexity. Sensor arrays are also attractive in a prosthetics context when exact muscle positions are unknown, such as may be the case after limb loss. We present preliminary data obtained while four amputee participants engaged in an abstract myoelectric decoding task. The decoder was controlled by muscles of the forearm or upper arm depending on the level of limb loss. We compare performance using a pair of surface electromyography sensors and while using a data driven weighting of eight sensors. Performance rates demonstrate that amputee participants are able to learn the myoelectric task. Results trend strongly toward enhanced performance when using multiple spatially weighted sensors. Further studies are required to test whether the use of additional myoelectric sensing hardware in abstract decoding would lead to effective prosthesis control.