## 2 Materials and Methods
### Data Management and Statistical Calculations  
The raw S-EMG signals (in Microvolts, uV) were filtered (using a band-pass filter (20-450 Hz)) to eliminate movement artefacts and other low-frequency noise, as well as high-frequency electrical noise. The filtered signals were then rectified by converting all negative values to positive and smoothed using a moving average to create an envelope of muscle activity. Normalised MVIC data were not used in the resulting graphs as the study focused on direct comparisons of muscle activation across bicycle configurations to identify the least taxing setup, prioritising practical relevance over relative activation percentages.  
We adopted a phase model to analyse the pedalling cycle, where a complete crank revolution was divided into four equal phases, each representing 25% of the total cycle duration. The pedalling cycle comprises two main movements: the downstroke and the upstroke. The downstroke phase occurs during the first 50% of the cycle, while the upstroke occupies the remaining 50%. By further subdividing these primary movements, we identified four distinct phases: two for the downstroke and two for the upstroke. During the first phase of the downstroke (Initial Downstroke (ID), 0-25%), the initial propulsion force is applied as the pedal moves downward from the top dead centre to the midpoint of the crank revolution. In the second phase of the downstroke (Terminal Downstroke (TD), 25-50%), the pedal continues its descent until it reaches the bottom dead centre. The first phase of the upstroke (Initial Upstroke (IU), 50-75%) encompasses the initial lift of the pedal from the bottom dead centre back towards the midpoint of the crank revolution. Finally, the second phase of the upstroke (Terminal Upstroke (TU), 75-100%) completes the cycle as the pedal returns to the top dead centre.  
The Statistical Parametric Mapping (SPM) or Statistical non-Parametric Mapping (SnPM) methods were utilised to compare muscle activity across different cycling positions and conditions, allowing for comparing muscle activation profiles throughout the pedalling cycle to identify significant differences and patterns. The muscle activation profiles were input into the SPM framework to perform a time-continuous statistical analysis, and a normality test (the Shapiro-Wilks statistic) was conducted to verify the data distribution [36]. The individual muscle activity profiles for the given conditions are illustrated in Figure 2A. The second graph (Figure 2B) represented the SPM results, indicating statistically significant differences in muscle activity across the pedalling cycle, with highlighted regions where \(p<0.05\). Finally, the third graph (Figure 2C) combined these data, showing the average muscle activity with standard error bars and the corresponding SPM results. The significant regions identified by SPM were marked with shaded areas and annotated \(p\)-values in a rectangle bar at the bottom of the graph. If the data were normally distributed, we analysed condition differences using ANOVA or a paired sample \(t\)-test of the SPM method. Alternatively, if the data were non-normally distributed, the Kruskal-Wallis ANOVA or U Mann-Whitney test was employed using the SnPM method. All data are reported as mean \(\pm\) standard deviation (SD). Statistical significance was accepted at the \(p<0.05\)level. MATLAB 2023b software (The MathWorks, Inc., Natick, MA, USA) was used for statistical analysis.  
The processed data were visualised using SPM plots to illustrate the muscle activation patterns assessed using test statistics.