Speaker
Description
Transcranial magnetic stimulation (TMS) is a powerful tool to modulate human brain function in a non-invasive fashion, with great potential for basic research and therapeutic applications. However, its effects may vary with pre-stimulus functional states (pFSs). This state dependency injects variability in TMS results, reducing their reproducibility and calling for experimental control. One way to exert such control is to measure a pFS of interest with electroencephalography (EEG), then use it to trigger TMS in real time. Unfortunately, pFSs tend to vanish before they can be fully processed. The solution to this problem is to forecast the EEG signal and program TMS to target the forecasted states, thereby compensating for processing delays. To this end, we have applied one traditional statistical model (univariate linear autoregression) and one generative deep learning model (WaveNet) to 64-channels task-related EEG data from 17 healthy young adults, obtaining accurate forecasts of attentional states up to 200 ms into the future. This work extends the traditional statistical model to previously untested horizons and explores the forecasting capabilities of deep learning approaches, opening doors for the use of complex pFS metrics in real-time EEG-TMS settings.
The forecasting study complements previous work on using convolutional neural networks (CNNs) to identify TMS-evoked activity from post-stimulus EEG data. Here, CNNs were trained on ~100.000 EEG samples at varying noise levels, reaching accuracies up to 96% and demonstrating an impressive robustness to a number of EEG artefacts. This result shows that TMS-evoked activity can be identified in an entirely automated fashion, potentially solving the problem of biased evaluations by human researchers. Taken together, the two studies describe a new way of doing brain stimulation research, where computational methods and large datasets help researchers overcome methodological challenges that have long prevented the establishment of TMS-EEG as a gold-standard technique.
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