In electroencephalography (EEG) data acquired in synchronization with functional magnetic resonance (fMRI), gradient residual spiking artifacts persisted after preprocessing using average template subtraction (AAS). There is a need for more accurate removal of residual spikes, so as to decrease the interference from frequency-based activity inferences, and less spurious correlations between time series.Aiming at the characteristics of spike artifacts in EEG data, this paper first uses the Schr-dinger method to decompose and identify the EEG data containing spikes, automatically subtracts most of the spike components with a large amplitude difference from the EEG, and then uses the amplitude threshold method to compensate the error by inverse compensation. Residual spikes with the same amplitude as the EEG are located to realize the location and removal of spike artifacts. For simulated signals, the signal amplitude error (Er) obtained by this method is 24.95% higher than that of the Schr-dinger method on average, and the signal-to-noise ratio (SNR) is 27.13% higher than that of the Schr-dinger method. For real signals, the Pearson correlation coefficient obtained by this method is significantly less than For the other four methods, the denoising effect is 11.42% higher than that of the Schrodinger method. Compared with other methods, the use of Schrodinger combined with threshold algorithm, significantly improved the peak recognition accuracy and the denoising effect, whether the peak is located in the trough of the waveform, or the high-frequency fluctuation amplitude is comparable to the peak. This denoising method provides strong support for the fusion study of EEG-fMRI.