14.2 EMG signal decomposition and analysis

EMG signals offer a window into muscle activity, revealing the intricate workings of motor units. These electrical signals, generated by muscle fibers, provide crucial insights into muscle function, activation patterns, and fatigue. Understanding EMG characteristics is key to unlocking the mysteries of human movement and neuromuscular control.

Processing and analyzing EMG signals involves various techniques, from decomposition to time and frequency domain analysis. These methods help researchers and clinicians extract valuable information about muscle behavior, fatigue, and motor unit recruitment. By interpreting EMG patterns, we can gain a deeper understanding of muscle function in health and disease.

EMG Signal Characteristics and Motor Units

Concept of motor unit action potentials

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• Motor unit consists of a motor neuron and the muscle fibers it innervates (alpha motor neuron, extrafusal muscle fibers)
• Activation of a motor unit causes all muscle fibers within the unit to contract simultaneously (all-or-none principle)
• MUAPs generated by the synchronous activation of muscle fibers within a motor unit
  • MUAP shape and amplitude depend on motor unit properties (number and size of muscle fibers, fiber type composition)
• EMG signals represent the summation of multiple MUAPs from different motor units (spatial and temporal summation)
  • Recruitment and firing rate of motor units determine overall EMG signal characteristics
  • Increasing muscle force achieved by recruiting more motor units and increasing their firing rates (Henneman's size principle, rate coding)

EMG Signal Processing and Analysis Techniques

Techniques for EMG decomposition

• EMG decomposition identifies individual MUAPs from the composite EMG signal
• Blind source separation techniques (independent component analysis, ICA) used for EMG decomposition
  • ICA assumes MUAPs are statistically independent and separates them based on unique spatial and temporal characteristics
• Template matching approach for EMG decomposition
  • MUAP templates created by averaging similar waveforms within the EMG signal
  • Templates used to identify and classify individual MUAPs throughout the signal
• EMG decomposition enables analysis of individual motor unit behavior and contributions to overall muscle activity

Time and frequency domain analysis

• Time-domain analysis provides information about EMG signal amplitude and duration of muscle activity
  • Root mean square (RMS) and integrated EMG (iEMG) quantify muscle activation levels
  • RMS: square root of the average power of the EMG signal over a specific time window $RMS = \sqrt{\frac{1}{N} \sum_{i=1}^{N} x_i^2}$, where $x_i$ is the $i$-th sample, and $N$ is the number of samples in the window
  • iEMG: area under the rectified EMG signal curve over a specific time interval $iEMG = \sum_{i=1}^{N} |x_i|$
• Frequency-domain analysis reveals information about muscle fatigue and fiber type composition
  • Power spectral density (PSD) assesses changes in EMG signal frequency content over time
  • Median frequency (MDF) and mean power frequency (MPF) are common frequency-domain features
    • MDF: frequency at which the PSD is divided into two equal energy regions
    • MPF: average frequency weighted by the power of the EMG signal $MPF = \frac{\sum_{i=1}^{N} f_i P_i}{\sum_{i=1}^{N} P_i}$, where $f_i$ is the $i$-th frequency component, and $P_i$ is the power at that frequency
  • Shift in PSD towards lower frequencies and decrease in MDF or MPF over time indicate muscle fatigue

Interpretation of EMG signal patterns

• EMG signal patterns provide insights into muscle activation, coordination, and fatigue
• Increased EMG amplitude indicates higher muscle activation levels (non-linear relationship, varies across muscles and individuals)
• EMG signal patterns reveal onset and duration of muscle activity during specific movements or tasks (timing and sequence of muscle activation, motor control strategies)
• Changes in EMG signal frequency content indicate muscle fatigue
  • Power spectrum shifts towards lower frequencies as muscles fatigue (changes in muscle fiber conduction velocity, recruitment of slower motor units)
• Analyzing EMG signal patterns with other biomechanical and physiological measures provides comprehensive understanding of muscle function and performance (force, kinematic data, metabolic measurements)