ePoster · Deep Learning
Adaptive probabilistic regression for real-time motor excitability state prediction from human EEG
Lisa Haxel, Jaivardhan Kapoor, Ulf Ziemann, Jakob Macke · Bernstein Conference 2024
Transcranial magnetic stimulation (TMS) is a promising tool for neuromodulatory interventions in research and clinical settings, yet its effects are highly variable. Real-time EEG-TMS seeks to mitigate this variability by analyzing electroencephalography (EEG) signals to determine the optimal timing for stimulation. However, existing EEG-informed TMS methodologies are predominantly open-loop, relying on predefined brain states and disregarding immediate and long-term stimulation effects [1, 2, 3]. Here, we propose an adaptive closed-loop TMS approach that allows dynamically adjusting stimula