## 1 Introduction  
In recent decades, great effort has aimed at identifying the threshold that delineates the aerobic-anaerobic transition during an incremental workout on a cycle ergometer [1; 2]. Such \"fatigue threshold\" has been calculated on the basis of metabolic, ventilatory, and surface electromyographic (EMG) variables. In particular, EMG-based thresholds are especially attractive because they can be used to characterize neuromuscular fatigue and can be assessed non-invasively [3]. In the time domain, the increase in neuromuscular fatigue during an incremental continuous exercise is manifested by an increase in EMG amplitude as a result of recruitment of additional motor units, increased firing rates, and/or synchronization [4]. Thus, EMG thresholds can be determined by examining the changes in the profile of the EMG amplitude during the test. EMG thresholds, however, have important limitations since the amplitude of the EMG signal is affected by many confounding factors [4], including (1) electrode location; (2) thickness of the subcutaneous tissues; (3) the detection system used to obtain the recording; (4) changes in the transmembrane action potential; (5) cross-talk from nearby muscles; and (6) signal cancellation. Due to all these confounding factors, caution is needed when using the EMG amplitude as an index of the level of muscle activation. One of the most common approaches to determine the EMG threshold is based on analyzing the increase in EMG amplitude throughout the whole duration of the incremental test by searching for breakpoints in the linearity of the EMG amplitude vs. time curve. The studies falling in this category vary in the number of breakpoints identified: Zero, one, or two [5]. Among all the factors influencing the identification of EMG breakpoints (for a review of the factors, see [PERSON] et al. 2016 [5]), the mathematical algorithm utilized is one of the most critical. Remarkably, only the algorithm proposed by [PERSON] et al. [6] was able to identify two EMG breakpoints in the EMG amplitude vs. time curve: The other automatic methods only identified the first breakpoint [7; 8; 9]. However, various studies that analyzed the EMG curve by visual analysis have confirmed the existence of two breakpoints [10; 11; 12]. Therefore, it would be interesting to test the algorithm proposed by [PERSON] et al. [6] using different configurations of the incremental cycling protocol (i.e., pedal cadence, the stage duration, the workload increment, etc.) to investigate the applicability and reliability of this method to find the first and second EMG thresholds. The multi-segment linear regression method proposed by [PERSON] et al. [6] was originally implemented in an incremental test where power output was increased by 5 W every 12 s. The short stage duration of this test makes it more similar to a ramp-like protocol compared to the graded steady state tests commonly used for EMG threshold detection. In the literature, there are graded steady tests with stage durations of 1, 2, and 3 min, although those studies that included EMG measures commonly selected 1 min step protocols [11; 13; 14]. More specifically, of the 27 studies analyzed in the review of [PERSON] et al. [5], 14 increased their workload every minute, two minute-protocols were used in five studies, while only two studies included a three-minute protocol. Moreover, incremental protocols including one-minute stages are recommended as the most appropriate ones for EMG threshold detection [5]. In addition, metabolic and ventilatory thresholds are commonly determined using incremental tests with one-minute stages [2; 15; 16; 17; 18]. Therefore, it is of interest to determine whether the multi-segment linear regression method is able to determine two breakpoints in the EMG curve using one-minute stages.