Mikael Laine

This paper introduces multichannel TMS (mTMS) for robust voxel-wise brain mapping, showing it outperforms single-coil TMS in accuracy and noise resilience.

This paper introduces an adaptive Bayesian method for localizing motor representation areas using TMS, reducing the number of stimuli needed for accurate localization.

The paper develops a method to predict TMS-evoked responses using pre-stimulus EEG features and finds that alpha, beta, and gamma oscillations correlate with TEP and MEP amplitudes.

This paper develops an automated algorithm for determining motor hotspots and thresholds in TMS, achieving faster and more accurate results than previous methods.

This paper characterizes an open-source electronic-robotic mTMS platform that improves the accuracy and automation of brain stimulation targeting.