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Consensus on the definition of common terms in electromyography (EMG) research promotes consistency in the EMG literature and facilitates the integration of research across the field. This paper presents a matrix developed within the Consensus for Experimental Design in Electromyography (CEDE) project, providing definitions for terms used in the EMG literature. The definitions for physiological and technical terms that are common in EMG research are included in two tables, with key information on each definition provided in a comment section. A brief outline of some basic principles for recording and analyzing EMG is included in an appendix, to provide researchers new to EMG with background and context for understanding the definitions of physiological and technical terms. This terminology matrix can be used as a reference to aid researchers new to EMG in reviewing the EMG literature.

Consensus on the definition of common terms in electromyography (EMG) research promotes consistency in the EMG literature and facilitates the integration of research across the field. This paper presents a matrix developed within the Consensus for Experimental Design in Electromyography (CEDE) project, providing definitions for terms used in the EMG literature. The definitions for physiological and technical terms that are common in EMG research are included in two tables, with key information on each definition provided in a comment section. A brief outline of some basic principles for recording and analyzing EMG is included in an appendix, to provide researchers new to EMG with background and context for understanding the definitions of physiological and technical terms. This terminology matrix can be used as a reference to aid researchers new to EMG in reviewing the EMG literature.

The general purpose of normalization of EMG amplitude is to enable comparisons between participants, muscles, measurement sessions or electrode positions. Normalization is necessary to reduce the impact of differences in physiological and anatomical characteristics of muscles and surrounding tissues. Normalization of the EMG amplitude provides information about the magnitude of muscle activation relative to a reference value. It is essential to select an appropriate method for normalization with specific reference to how the EMG signal will be interpreted, and to consider how the normalized EMG amplitude may change when interpreting it under specific conditions. This matrix, developed by the Consensus for Experimental Design in Electromyography (CEDE) project, presents six approaches to EMG normalization: (1) Maximal voluntary contraction (MVC) in same task/context as the task of interest, (2) Standardized isometric MVC (which is not necessarily matched to the contraction type in the task of interest), (3) Standardized submaximal task (isometric/dynamic) that can be task-specific, (4) Peak/mean EMG amplitude in task, (5) Non-normalized, and (6) Maximal M-wave. General considerations for normalization, features that should be reported, definitions, and "pros and cons" of each normalization approach are presented first. This information is followed by recommendations for specific experimental contexts, along with an explanation of the factors that determine the suitability of a method, and frequently asked questions. This matrix is intended to help researchers when selecting, reporting and interpreting EMG amplitude data.

This paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four diferent isometric tasks at diferent efort levels associated with movements of the forearm. Three 2-D electrode arrays were used for recording the myoelectric activity from fve upper limb muscles: biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres. Technical validation comprised a signals quality assessment from outlier detection algorithms based on supervised and non-supervised classifcation methods. About 6% of the total number of signals were identifed as “bad” channels demonstrating the high quality of the recordings. In addition, spatial and intensity features of HD-sEMG maps for identifcation of efort type and level, have been formulated in the framework of this database, demonstrating better performance than the traditional time-domain features. The presented database can be used for pattern recognition and MUAP identifcation among other uses., Peer Reviewed, Postprint (published version)