EMG Signal Processing: Techniques & Recommendations

Electromyography (EMG) captures valuable data about muscle activity, but the raw signal is noisy, variable, and difficult to interpret without proper processing. This article outlines the most common EMG processing techniques, explains when and why to apply them, and incorporates practical implementation details from Noraxon’s MR software platform. Filtering: The First Step. Why Filter?. Filtering is essential for cleaning up raw EMG signals by removing unwanted noise and signal artifacts. Surface EMG is susceptible to both:. Low-frequency noise such as motion artifacts and DC offset, High-frequency noise such as ambient electrical interference. So Why Does This Matter For EMG Analysis?. The answer is simple — motion artifact interferes with onset baseline evaluation and amplitude. Therefore, a high-pass filter may be needed any time motion artifact is noticed. This can be when:. Recording high-movement activities (e.g. pitching, jumping)., Using wireless EMG systems with cables or housing attached, as heavier objects attached to the electrode cause more motion artifact., Analyzing onset timing, where baseline stability matters. Tradeoff: A higher cutoff may distort low-intensity muscle signals, so it’s a balance between noise reduction and preserving valid EMG content. What is Rectification?. Rectification converts the EMG signal to absolute values, so all negative voltages become positive. This simplifies analysis and enables averaging. If a signal is not rectified before analyzing the amplitude, positive and negative values would otherwise cancel out., In MR, rectification is applied after filtering and before smoothing. When Rectification Is Needed. Rectification is still required if you’re using:. Moving average of mean absolute smoothing, Calculating features like mean amplitude or area under the curve.