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Automated EMG Analysis

AcqKnowledge includes a number of powerful automated EMG analysis features.

Derive Average Rectified EMG  

Derive Average rectified EMG (ARV) is defined as a time windowed mean of the absolute value of the signal. ARV is one of the various processing methods used to construct derived signals from raw EMG data that can be useful for further analysis.

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Derive Integrated EMG

Integrated EMG (iEMG) is defined as the area under the curve of the rectified EMG signal, that is, the mathematical integral of the absolute value of the raw EMG signal. When the absolute value of the signal is taken, noise will make the mathematical integral have a constant increase. Integrated EMG splits up the signal into fixed-width timeslices and resets the integral at the start of each timeslice. To derive iEMG, the width of this timeslice must be specified. The integrated rectified EMG signal will appear like a “sawtooth” style wave. In addition to the true iEMG a second waveform whose value is the maximum value of the iEMG signal in each timeslice will also be output. This Maximum iEMG is easier to interpret visually and approximates the envelope of the iEMG signal.

Root Mean Square (RMS) EMG Data

Root Means Square EMG

Root Mean Square EMG (RMS EMG) is defined as the time windowed RMS value of the raw EMG. RMS is one of a number of methods used to produce waveforms that are more easily analyzable than the raw EMG.

EMG Frequency & Power Analysis  

Several frequency domain techniques may be used for data reduction of EMG signals. The EMG Frequency & Power Analysis script extracts several measures derived from the power spectrum of an EMG signal. The EMG signal is split up into a fixed number of time periods; within each window, the power spectrum is computed using the Power Spectral Density transformation. For each time period, the following measures are extracted: Median Frequency, Mean Frequency, Peak Frequency, Mean Power, and Total Power.Muscle Activation data

Locate Muscle Activation

When performing gait analysis, exercise physiology, startle response, or other research, identification of periods where the muscle is active can allow for correlation of external factors to muscle activity. Locate Muscle Activation attempts to identify various periods of muscle activity using statistical methods. The transformation requires a raw, unfiltered surface EMG channel. It is important that the muscle being examined is relaxed for the first 0.25 seconds of the recording to provide an estimate of the ‚background noise‚ during areas of muscle relaxation. This quarter-second period is used to estimate baseline parameters that affect the entire process.

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