Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a non-invasive technique for studying the excitability and connectivity of the brain cortex, which has been applied in basic scientific research and clinical disease assessment. However, the applicability of TMS-EEG is limited due to artifacts of both biological and abiotic nature induced by TMS pulse output. To address this issue, in the past decade, researchers have developed various TMS-EEG artifact removal methods from two perspectives, including online experimental design and offline data post-processing, and have achieved numerous valuable results. In this paper, the currently available TMS-EEG artifact removal methods are reviewed to facilitate the application of TMS-EEG in basic research and clinical disease diagnosis and treatment.

Magneto-acoustic-electric tomography (MAET) boasts high resolution in ultrasound imaging and high contrast in electrical impedance imaging, making it of significant research value in the fields of early tumor diagnosis and bioelectrical monitoring. In this study, a method was proposed that combined high conductivity liquid metal and maximum length sequence (M sequence) coded excitation to improve the signal-to-noise ratio. It was shown that, under rotational scanning, the liquid metal significantly improved the signal-to-noise ratio of the inter-tissue magneto-acoustic-electric signal and enhanced the quality of the reconstructed image. The signal-to-noise ratio of the signal was increased by 5.6, 11.1, 21.7, and 45.7 times under the excitation of 7-, 15-, 31-, and 63-bit M sequence code, respectively. The total usage time of 31-bit M sequence coded excitation imaging was shortened by 75.6% compared with single-pulse excitation when the same signal-to-noise ratio was improved. In conclusion, the imaging method combining liquid metal and M-sequence coding excitation has positive significance for improving MAET image quality.
Contrast Media ; Electricity ; Electric Conductivity ; Acoustics ; Tomography