## 1 Introduction  
Electromyogram (EMG) signals are superimposed motor unit action potentials (MUAPs) generated by motor neuron firing, reflecting a high level of neural control signals from the brain, and can be decoded by change-point detection algorithms to provide very specific information on the timing of muscle action [1]. The temporal information of muscle action can be used as a reliable neural control signal for mechanical exoskeletons [2; 3], as a source of information for clinical evaluation of neuromuscular injuries [4; 5], as a reference indicator for sports training [6; 7], and as an interface signal for human-computer interaction [8; 9; 10]. It has important research significance and application value. The most common method for EMG change-point detection (CPD) is the threshold method based on the time domain, which is very simple and intuitive [11]. There is a consensus among researchers that the amplitude variation of the EMG signal can reflect the activation intensity of MUAPs and the duration of muscle activity [12]. Therefore, comparing the EMG signal with a fixed threshold, the time point recorded when the amplitude of the EMG signal is above or below this threshold reflects the temporal characteristics of muscle activation. However, in situations where the threshold level is derived from the EMG amplitude, any change in the electrical activity amplitude will proportionally affect the threshold level [13]. Therefore, a fixed-constant threshold is often unsatisfactory for the CPD of noisy EMG signals. To overcome this problem, a double-threshold method was proposed. Double-threshold detectors allow the user to adopt the link between false alarms and detection probability with a higher degree of freedom than single-threshold methods. The user can tune the detector according to different optimal criteria, adaptingits performance to the characteristics of each specific signal and application. However, double-threshold detectors still rely on complex signal preprocessing techniques, and they require prior acquisition of the features of the baseline segment (noise only) of the EMG signals. Consequently, frequency domain-based methods such as short-time Fourier transform (STFT) [14; 15] and wavelet transform [16; 17; 18; 19] are used to deal with the CPD issue of EMG and exhibit strong anti-noise ability. However, the time-to-frequency conversion of the frequency-domain-based methods requires a long window length to ensure a certain frequency-domain resolution, so the detector has poor real-time performance and is prone to distortion at high frequencies. With the development of neural network technology, dealing with CPD issues has refocused on the time-domain of the signals. The neural network detectors learn the time-domain features of the signal, which not only solves the real-time problem of the frequency-domain approach but also achieves strong anti-noise ability compared with the threshold-based method. However, such methods require a certain amount of labelled training data to train the parameters for optimal performance, and they also need to avoid problems such as overfitting or underfitting. It is difficult to obtain data with accurate labels due to the instability of human biosignals. Therefore, although the neural network detectors have good CPD performance in experimental scenarios [20; 21], they are still not suitable for practical applications without solving the robustness of few-shot learning. Recently, published statistical methods, including the Bayesian change-point analysis methodology [22], profile likelihood maximization (PLM) [23; 24], and CUSUM [25], are attractive alternatives. However, prior knowledge of the distribution assumption, probability parameter estimation, and density function selection can seriously affect the performance of CPD.