title: Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition.
abstract: Accurate descriptors of muscular activity play an important role in clinical practice and rehabilitation research. Such descriptors are features of myoelectric signals extracted from sliding time windows. A wide variety of myoelectric features have been used as inputs to pattern recognition algorithms that aim to decode motor volition. The output of these algorithms can then be used to control limb prostheses, exoskeletons, and rehabilitation therapies. In the present study, cardinality is introduced and compared with traditional time-domain (Hudgins' set) and other recently proposed myoelectric features (for example, rough entropy). Cardinality was found to consistently outperform other features, including those that are more sophisticated and computationally expensive, despite variations in sampling frequency, time window length, contraction dynamics, type, and number of movements (single or simultaneous), and classification algorithms. Provided that the signal resolution is kept between 12 and 14 bits, cardinality improves myoelectric pattern recognition for the prediction of motion volition. This technology is instrumental for the rehabilitation of amputees and patients with motor impairments where myoelectric signals are viable. All code and data used in this work is available online within BioPatRec.
authors.affiliation: Department of Signals and Systems, Chalmers University of Technology Gothenburg, Sweden ; Centre for Advanced Reconstruction of Extremities, Sahlgrenska University Hospital Gothenburg, Sweden ; Integrum AB Gothenburg, Sweden.
authors.forename: Max
authors.initials: M
authors.lastname: Ortiz-Catalan
keywords: EMG; bioelectric signal processing; cardinality; electromyography; myoelectric pattern recognition; prosthetic control