Module 3D

Signal Definition: EMG (electromyography) is defined as an alternating current that is a variable signal. It has a frequency component, meaning its occurrences can be quantified.
Frequency Characteristics: An EMG signal exhibits a frequency that describes how often the signal cycles happen over a period of time, typically denoted in hertz (Hz).
- Example: A snapped cable yields a low frequency signal of 1 to 6 Hz.
- Background Frequencies: Other common frequencies might include those of electrical systems, which operate at 50 or 60 Hz depending on geographical location.
- Noise Management: There is often high-frequency noise in EMG readings that must be filtered out to focus on the targeted frequencies of interest.

Initial processing of the raw EMG signal requires cleaning and smoothing to make data extraction meaningful.
- Importance of Smoothing: Smoothing allows handling of large amounts of data more effectively.
Rectification of EMG: The rectangular shape of muscle activity in the EMG occurs due to rectification, which converts negative values to positive, as EMG signals are AC signals. If averaged straightforwardly without rectification, results would return to zero.

Rectification: Convert the EMG from negative to positive values.
Smoothing: Apply a smoothing technique, commonly the Root Mean Square (RMS).
- RMS Definition: This involves taking the square root of the mean of the squared values:
  - $ext{RMS} = \frac{1}{N} ext{(Sum of squares of values)}; ext{then take the square root}$
- Window Length: The window length chosen for the RMS impacts the smoothness of the output.
  - Longer Windows: Yield a smoother representation of the data but may obscure rapid changes.
  - Shorter Windows: Preserve rapid fluctuations and details in the data.
Integrated EMG (IEMG): Integration of the rectified EMG signal involves calculating the area under the curve.
- Integral Representation: $ext{IEMG} = ext{Area under the rectified EMG curve}$
- Outcome: As IEMG accumulates, it provides insight into cumulative muscle activity over time.

Isometric vs. Dynamic Contractions: Reliability of EMG measurements varies with contraction type.
- Isometric Contraction: Allows use of shorter window lengths without sacrificing smoothness, enhancing reliability.
  - Improved reliability metrics can include lower coefficients of variation as window lengths increase.
- Dynamic Contraction: Requires smaller window lengths for fidelity in measurement, leading to overall lower reliability in EMG studies compared to isometric findings.

Frequency Domain Analysis: Involves examining the amplitude of a signal against its frequency.
- Power Spectrum Representation: Gives insight into how muscle units activate.
  - As fatigue occurs, the frequency shifts to lower values.
- Fatigue Index: Compiles findings from initial, middle, and later segments of a fatiguing isometric contraction to illustrate how EMG frequency declines with time.
  - E.g., mean or median frequency change from higher to lower values indicates fatigue and reduced conduction velocity amongst muscle fibers.

Onset Detection: Analyzing the onset and timing differences in muscle activation due to various stimuli.
- Relevant in contexts such as concussion research and shoulder/low back injury assessments.
Myoelectric Activity Measurement: Examining levels of muscle activation individually, without normalization, when within-person comparisons occur on the same muscle.
- Notably, differences in muscle lengths and ranges of motion might lead to misinterpretations about performance adaptations based solely on EMG activity.
- Key Point: Higher myoelectric activity does not necessarily correlate with superior adaptation; force production is also a significant contributor.
Coordination and Movement Timing: Investigating how muscles coordinate actions and timing in movements.
- Less impacted by variations in activity versus force relationships.
- Helps in understanding patterns of muscle recruitment and movement efficiency.

Importance of reading scholarly papers that outline operational definitions and empirical research methods.
- This literature helps clarify valid uses of EMG, highlighting essential variables that researchers need to control for in studies.
Consideration of applicability for surface EMG findings is vital, as this will be relevant for assessments and testing involved in the course.

Familiarization with EMG signal processing and analysis methods sets the foundation for future assessments. Understanding the implications of contraction types and the relationships among EMG activity, force production, and muscle adaptation will guide practical applications in research and clinical assessments.