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Valen, K.

Publications and source records attributed to Valen, K..

2 recordsLinked to original sources

A High-Precision Timing Method and Digital Interface for Closed-Loop TMS

ObjectiveCurrent transcranial magnetic stimulation (TMS) protocols exhibit high inter-subject variability in treatment outcomes, highlighting the need for personalized, brain-state-dependent closed-loop stimulation protocols. To enable such protocols, we aim to provide robust, precisely timed external control of TMS, with stimulation timed relative to feedback signals such as the electroencephalogram (EEG). ApproachCommercial TMS devices typically rely on trigger signals for precise external pulse timing, while adjusting stimulation parameters, such as intensity, is better handled via serial digital communication, which supports robust error detection and feedback. However, combining these communication methods is inherently complex and prone to timing issues, such as race conditions. Furthermore, trigger signals lack capabilities essential for real-time systems, such as preventing late pulse delivery. We present a method for precise and accurate pulse timing, implemented through a digital interface that uses exclusively serial digital messaging, eliminating the need for trigger signals. This interface enables external control of pulse timing, intensity, and other parameters. The TMS device maintains its own internal clock and delivers pulses at pre-scheduled times, decoupling timing precision from the control device. Additionally, we propose a method for synchronizing such time-tracking TMS devices with commercial EEG systems, enabling precisely timed EEG-TMS. Main resultsUsing these methods, our custom TMS device delivered pulses precisely aligned to the EEG signal, with timing errors consistently below 0.3 ms. These errors were constrained by the experimental setup, including the sampling rate of our EEG device and the signal-to-noise ratio affecting pulse detection. SignificanceOur timing method achieves sub-millisecond precision in brain-state-dependent closed-loop EEG-TMS, providing a foundation for robust TMS timing that supports adaptive, personalized stimulation protocols. The digital control interface, co-designed with our TMS device, integrates pulse timing and parameters, setting a precedent for future advancements in computer-controlled TMS.

bioengineering↗

NeuroSimo: an open-source software for closed-loop EEG- or EMG-guided TMS

ObjectiveOur goal was to create open-source software for closed-loop EEG-TMS that allows researchers to rapidly prototype and develop novel stimulation paradigms in a high-level programming language. This addresses the limitations of current solutions, which often rely on proprietary hardware and software, limiting their accessibility and customizability, or comprise ad-hoc pipelines tailored to specific use cases. ApproachWe developed NeuroSimo, a software platform that enables arbitrary EEG-TMS stimulation protocols written in Python, leveraging Pythons ecosystem of scientific, neuroimaging, and machine learning libraries. The core software is written in C++ with an embedded Python interpreter and employs the Robot Operating System (ROS 2) for inter-process communication. NeuroSimo runs on real-time-enabled Linux Ubuntu, using LabJack T4 for pulse triggering, and supports two EEG device models (Bittium NeurOne, BrainProducts actiCHamp) and TMS devices that deliver pulses via trigger signals. The software includes a graphical user interface for configuration and performance monitoring, and supports GPU processing for neural network computations. Main resultsIn brain-state-dependent stimulation using the Phastimate algorithm, which targets TMS pulses to the trough of sensorimotor -rhythm, NeuroSimo achieved a median timing error of 0.2 ms (95% CI: 0.2-0.2 ms), a 99th-percentile of 0.6 ms (0.6-0.6 ms), and a maximum of 1.4 ms. SignificanceAs an open-source platform combining Pythons flexibility with real-time closed-loop EEG-TMS, NeuroSimo enables researchers to develop and implement novel therapeutic approaches, marking a significant advance in personalized brain stimulation.

bioengineering↗