Fast denoising of multi-channel transcranial magnetic stimulation signal based on improved generalized mathematical morphological filtering

Liu, Jinzhen; Tian, Kaiwen; Xiong, Hui; Zheng, Yu

doi:10.1016/j.bspc.2021.103348

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Group 1 of the simulation dataset is used for the simulation dataset, and the first public dataset in this paper is used for the public dataset. Note that for field signals, there is no clean signal to use as a reference (Liu et al 2022a(Liu et al , 2022b. This experiment used the resting EEG as a clean standard signal.…”

Section: Comparison With Tms-eeg Data Analysis Toolboxesmentioning

confidence: 99%

“…Accurate labeling of TMS pulses facilitates subsequent identification and elimination of pulse artifacts. However, manual EEG labeling is a time-consuming and subjective task, and the TMS pulses cannot be accurately labeled using an external marker (Zrenner et al 2020, Liu et al 2022a, 2022b. In addition, the interpolation window must be automatically determined in accordance with the various pulse artifacts that are present.…”

Section: Introductionmentioning

confidence: 99%

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A three-dimensional adaptive rational interpolation algorithm for removing TMS-EEG pulse artifacts

Xiong,

Di,

Liu

et al. 2023

Physiol. Meas.

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Objective. Transcranial magnetic stimulation in combination with electroencephalography (TMS-EEG) has been widely used to study the reactivity and connectivity of brain regions. In order to efficiently and fast solve the pulse artifacts problem caused by TMS electromagnetic pulses, a three-dimensional adaptive rational quadratic Hermite interpolation algorithm is proposed. Approach. Firstly, a three-dimensional signal matrix is obtained by a signal recombination algorithm, where the removed window is automatically obtained by a derivative threshold. Secondly, the adaptive rational quartic Hermite interpolation algorithm is used to interpolate the removed window. Finally, the performance of the algorithm is verified using simulated and public database data. Main results. The simulation results show that the proposed algorithm improves the SNR by 23.88%~47.60%, reduces the RMSE by 46.52%~81.11%, reduces the average MAE by 47.83%~58.33%, and reduces the time consumption of the proposed algorithm by 45.90% compared with the piecewise cubic Hermite interpolation algorithm. Significance. Therefore, TMS-EEG pulse artifacts can be removed effectively and quickly with the proposed algorithm.

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Section: Comparison With Tms-eeg Data Analysis Toolboxesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%