Transcranial magnetic stimulation (TMS) is a method by which the function of motor cortical networks can be non-invasively tested. It is gaining increased attention for its ability to detect neuronal network dysfunction in neurodegenerative diseases, including Amyotrophic Lateral Sclerosis (ALS). Research studies harnessing TMS have revealed that people with ALS exhibit motor cortical hyperexcitability, likely due to loss of inhibitory control of the upper motor neurons. The TMS-based measures which capture this hyperexcitability have been proposed as potential early, or even presymptomatic, diagnostic biomarkers of ALS. However, previous research has also demonstrated that the amplitude of motor evoked potentials (MEPs), which form the basis of these TMS measures, is influenced by precontraction and baseline EMG variation in the muscle of interest. In order to control for the effects of such muscle activity, participants of TMS studies are typically required to maintain low baseline EMG activity (<5-10μV). However, the effects of EMG amplitude variation below this threshold on TMS measures have been insufficiently explored. This variation could represent an important lurking variable in the measurement of diseases, particularly those which can cause abnormal muscle activation at rest, such as ALS. We hypothesise that TMS-based measurements used to index cortical hyperexcitability in people with ALS and other neurodegenerative diseases may be affected by low amplitude baseline muscle activation. In this project, I aim to determine whether baseline EMG amplitude affects single and paired pulse TMS measures, including MEP amplitude, short intracortical inhibition (SICI), long intracortical inhibition (LICI), and intracortical facilitation (ICF), both in controls and in people with ALS. I also aim to determine to what extent that baseline muscle activity explains the apparent difference in cortical excitability between people with ALS and controls. Such understanding of the pathophysiology driving these proposed ALS biomarkers is essential to avoid incorrect clinical application.