Transcranial magnetic stimulation (TMS) is an established method to treat various neurological diseases, such as depression, Alzheimer’s disease, and tinnitus. New applications for TMS are closed loop neurofeedback (NF) scenarios, which require software control of the TMS system, instead of the currently used manual control. Hence, the MagCPP (https://github.com/MagCPP) toolbox was developed and is described in this work. The toolbox enables the external control of Magstim TMS devices via a C++ interface. Comparing MagCPP to two other toolboxes in a TMS application scenario with 40% power, we found that MagCPP works faster and has lower variability in repeated runs (MagCPP, Python, MATLAB [mean±std in seconds]: 1.19±0.00, 1.59±0.01, 1.44±0.02). An integration of MagCPP in a real-time data processing platform MNE-CPP with an optional GUI demonstrates its ability as part of a closed-loop NF-scenario. With its performing advantages over other toolboxes, MagCPP is a first step towards a complete closed loop NF scenario and offers possibilities for novel study designs.
Depth-based plethysmography (DPG) for the measurement of respiratory parameters is a mobile and cost-effective alternative to spirometry and body plethysmography. In addition, natural breathing can be measured without a mouthpiece, and breathing mechanics can be visualized. This paper aims at showing further improvements for DPG by analyzing recent developments regarding the individual components of a DPG measurement. Starting from the advantages and application scenarios, measurement scenarios and recording devices, selection algorithms and location of a region of interest (ROI) on the upper body, signal processing steps, models for error minimization with a reference measurement device, and final evaluation procedures are presented and discussed. It is shown that ROI selection has an impact on signal quality. Adaptive methods and dynamic referencing of body points to select the ROI can allow more accurate placement and thus lead to better signal quality. Multiple different ROIs can be used to assess breathing mechanics and distinguish patient groups. Signal acquisition can be performed quickly using arithmetic calculations and is not inferior to complex 3D reconstruction algorithms. It is shown that linear models provide a good approximation of the signal. However, further dependencies, such as personal characteristics, may lead to non-linear models in the future. Finally, it is pointed out to focus developments with respect to single-camera systems and to focus on independence from an individual calibration in the evaluation.
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