Skeletal Muscular Patterning: Topographical Analysis of the Integrated Electromyogram

Abstract

The extraction of mean amplitude from integrated electromyographic (IEMG) responses provides a valid but limited measure of muscle action potentials (MAPs). More comprehensive topographical analyses of IEMG responses based on frequency analyses are unsatisfactory since IEMG responses are aperiodic, and autoregressive procedures may be unsatisfactory since autocorrelations constitute an irreversible transformation of the original waveform, represent a substantial loss of information in contrast to the original waveform, and harbor little apparent relationship to MAPs. In a Theoretical Investigation, a mathematical procedure is developed to quantify topographical features of both the amplitude and temporal dimensions of the IEMG response. Procedures for extracting these parameters and the relationship between each and the recruitment of MAPs are illustrated. In a Validation Study, surface electromyograms are recorded from the preferred forearm under various conditions of mild isometric forearm contraction. Results support the viability, reproducibility, and validity of the topographical analysis.

Affective signal processing (ASP) : unraveling the mystery of emotions

“…To the authors best knowledge, the skewness (and kurtosis) of EMG signals as discriminating descriptor have been discusses in only three studies. In 1983, Cacioppo, Marshall-Goodell and Dorfman [82] analyzed among a number of parameters, the skewness and kurtosis of skeletal muscle patterns, recorded through EMGs. Four years later, an article by Cacioppo and Dorfman [81] that discussed "waveform moment analysis in psychophysiological research" in general.…”

“…Apparently, differences in emotions or feelings are usually reflected in various statistical parameters, but not necessarily in all of the ones tested (cf. [81,82,278]). Why the effect over all statistical parameters only occurred for the zygomaticus major activity in the present experiment is not a priori clear; maybe effects of signal resolution played a role, maybe it is related to the fact that positive emotions are more overtly expressed in our culture [450].…”

Waveform moment analysis: An ASYST program for topographical analyses of nonnegative bounded waveforms

“…:x; see Dorfman & Cacioppo, in press). When dealing with simple (e.g., unimodal) waveforms, the classic shape descriptors of mean, variance, skewness, and kurtosis, which are based on the lower-order moments, often provide satisfactory characterizations of these waveforms; however, when dealing with more complex waveforms, these classic shape descriptors can provide nondiscriminating characterizations of clearly distinctive waveforms (see Cacioppo et al, 1983). With the ready availability of the high-speed digital computer and efficient computational procedures such as those provided by WAMA, one might profitably compute higher-order indicants of asymmetry and dispersion to derive more complete summaries of these features of NB waveforms.…”

“…This is a broad class of waveforms, including learning acquisition curves and integrated electromyographic responses in the time domain, amplitude distributions of the electroencephalogram and distributions of tests scores, and power spectra of various physical, behavioral, and bioelectric signals. Quantitative representations of NB waveforms have traditionally been insensitive to substantive features of the topography of those waveforms (see Cacioppo, Marshall-Goodell, & Dorfman, 1983). Cacioppo and Dorfman (1987) recently developed a quantitative representation of NB waveforms predicated on the moments of the waveform in a given domain.…”

Microcomputers in social psychophysiological research: An overview

“…"Relatively slow" in this context means that we may spend several hours extracting the first five moments from the trial-by-trial EMG waveforms, and another hour computing second-by-second heartrate data from a single subject. Furthermore, this system is unable to process numbers with more than 32-bit precision during intermediate calculations (see Cacioppo, Marshall-Goodell, & Dorfman, 1983, for a description of some of these analytic procedures) .…”