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In transcranial magnetic stimulation (TMS) and electroencephalography (EEG) experiments, two researchers typically collaborate in the lab. This study addresses the challenge a single researcher faces in managing the TMS experiment's timing while operating the TMS coil. It introduces the Arduino Trigger Generator (ArTGen) to remotely control the timing of TMS experiments using a footswitch pedal. Moreover, a bespoke printed circuit board (PCB) is designed to interface the eegoMylab amplifier with off‐the‐shelf EEG caps. The ArTGen facilitates accurate timing of the TMS stimulator's inter‐pulse intervals (IPIs) through a footswitch pedal, enhancing researchers' control over TMS‐EEG experiments. The PCB interface provides a cost‐effective tool to extend the functionality of the eegoMylab amplifier. The integration of our PCB interface has been validated in a custom TMS‐EEG setup by analyzing TMS‐evoked potentials (TEPs), global mean field power (GMFP), butterfly plots, and event‐related spectral potentials (ERSPs). The PCB reliably preserved EEG signal integrity, ensuring accurate data acquisition. Thorough channel‐wise consistency checks across components confirmed data accuracy. ArTGen's portability and footswitch feature streamline experimental control, aiding TMS‐EEG research and clinical applications. Moreover, our PCB resolves compatibility between the eegoMylab amplifier and the Waveguard EEG cap by extending the amplifier to connect to off‐the‐shelf EEG caps. The ArTGen serves as a robust remote control tool for TMS stimulators, while our PCB interface presents a solution for integrating a customized TMS‐EEG setup. This study addresses the gap in existing TMS‐EEG research by introducing innovative technological enhancements that not only augment experimental flexibility but also streamline procedural workflows. [ABSTRACT FROM AUTHOR]

Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects. [ABSTRACT FROM AUTHOR]