## 2 Materials and Methods
### Data Processing  
(1) Data Collection and Processing  
The data in this experiment included questionnaire scores, behavioral data, and ERP data. Behavioral data consisted of reaction times (RTs) and accuracy (ACC), recorded by E-prime 3.0 software. Behavioral data were statistically analyzed using SPSS 27.0 (IBM Corporation, Armonk, NY, USA) and E-Prime 3.0.  
EEG signals were recorded using the international 10-20 system with a Neuroscan Quickcap electrode cap containing 64 Ag/AgCl electrodes (NeuroScan, El Paso, TX, USA). The sampling rate was set to 1000 Hz, and a real-time bandpass filter of 0.1-30 Hz was applied [23]. The analysis epoch was set to 1000 ms, including 200 ms before stimulus onset and 800 ms after stimulus onset. Independent component analysis (ICA) was used to remove ocular artifacts, and trials containing motion artifacts were excluded using a 75 uV threshold. EEG data preprocessing was conducted using EEGLAB2020 (Swartz Center for Computational Neuroscience, University of California San Diego, CA, USA). First, unnecessary electrodes (\"CB1\", \"CB2\", \"HEO\", \"VEO\", \"EKG\", \"EMG\", and \"TRIGGER\") were removed. For segmented signals, M1-M2 referencing was applied. Signal segments containing artifacts from eye movements (EOG amplitude greater than 50 uV), muscle activity (amplitude greater than 100 uV at other electrodes), or erroneous responses were excluded. ICA was then performed on the remaining signal segments, retaining 30 independent components. Twelve electrodes of interest were selected, including the frontal region (F3, FZ, F4), central region (C3, CZ, C4), parietal region (P3, PZ, P4), and occipital region (PO7, OZ, PO8) electrodes. The P200 time window was selected as 180-280 ms after stimulus presentation, and the P300 time window was selected as 300-450 ms after stimulus presentation, ensuring that the peak points of the waveforms fell within the specified time windows. For the same experimental condition (task interruption group, pause interruption group, and baseline group), trials of the same type were averaged. After artifact removal, the average acceptance rate for the trials used for averaging was 90.32%.  
(2) Data Analysis  
For behavioral data, the relationship between task interruption, pause interruption, and fatigue was first examined using a repeated-measures ANOVA with a 2(fatigued, non-fatigued) \(\times\) 3(simple task interruption, complex task interruption, pause interruption) \(\times\) 2(pre-interruption trials, recovery trials) design, to evaluate the cumulative effects of fatigue. Subsequently, the relationship between fatigue and task interruption complexity was analyzed using a 2(fatigued, non-fatigued) \(\times\) 2(simple task interruption, complex task interruption) \(\times\) 3(pre-interruption trials, interruption trials, recovery trials) repeated-measures ANOVA.  
For ERP data, P200 and P300 mean amplitudes and peak latencies were analyzed. A 2(fatigued, non-fatigued) \(\times\) 3(simple task interruption, complex task interruption, pause interruption) repeated-measures ANOVA was conducted, followed by a 2(fatigued, non-fatigued) \(\times\) 3(simple task interruption, complex task interruption, pause interruption) \(\times\)3(pre-interruption trials, interruption trials, recovery trials) repeated-measures ANOVA, to analyze the differential effects of various interruption types on neural responses before and after interruptions across the frontal, central, and parietal regions.  
Two participants' data were excluded due to excessive eye movement and motor artifacts during the experiment, as well as poor task accuracy. Data from the remaining 38 participants were included in the final analysis. For data that did not meet the sphericity assumption in ANOVA, Greenhouse-Geisser corrections were applied. The significance level was set at \(p=0.05\), and effect size was reported using partial eta squared (\(\eta_{p}^{2}\)).