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Acoustic or visual warning signals for workers in hazardous situations might fail under loud and/or lowvisibility work situations. A warning system that uses electrocutaneous stimulation can overcome this problem. This study aimed to compare vertical, diagonal, and horizontal current stimulation directions at the upper arm to select the one with the lowest amount of muscle twitching. Fourteen electrodes were attached in two rows to the upper right arm of 15 participants. The stimulation was conducted with bi-phasic rectangular pulses of 150 μs and amplitudes of up to 25 mA. Muscle twitch thresholds have been determined and a circumferential stimulation signal was presented as warning pattern for the three current stimulation directions and evaluated regarding alertness, discomfort, and urgency. For single stimulation pulses, muscle twitches occurred slightly less often at the horizontal stimulation direction compared to the other two and muscle twitch thresholds showed no systematic differences. For the warning patterns, no considerable differences were found regarding the evaluation of alertness, discomfort, and urgency and no differences were found for muscle twitching. In conclusion, all orientations seem suitable for warning pattern presentation and none of the directions has a clear advantage in reducing muscle twitch.

There is a variety of software packages, toolboxes, or libraries for the analysis and processing of neurophysiological data such as EEG and MEG. Many of these solutions provide algorithms for both, sensor-space analysis and sourcespace analysis. Especially with the solutions that run on Windows machines, it is noticeable that the step of the volume model generation is usually not included, since the state-ofthe- art software for this (FreeSurfer) is a Unix-based software and thus not available forWindows machines. Therefore, our goal was to develop a fully-integrated software solution for Windows machines, accessing all processing steps already implemented in an existing toolbox and using FreeSurfer in the same system. Due to its widespread use, we chose MNE Python as the basis for our fully integrated software solution. We used the Windows Subsystem for Linux to create a virtual Linux kernel for the FreeSurfer installation. To demonstrate the workflow, the libeep, and AutoReject libraries have been added. A 64-channel EEG recording during right-hand movement (ME) and imagination (MI) was used to test the implemented workflow. The developed framework consists of several modules within Python, mainly using existing scripts and functions. The library libeep was integrated to read the EEG data with the ‘.cnt’, eeprope format. AutoReject was used to automatically interpolate detected bad channels or to reject complete epochs. FreeSurfer was successfully integrated and customized Python scripts enabled the communication between MNE Python on a Windows machine and FreeSurfer on a virtual Linux kernel. With the above-mentioned EEG dataset, we performed source reconstruction and were able to show ERD/S patterns for both, ME and MI. Our new, fullyintegrated software framework can be used on Windows machines to perform a complete process of source reconstruction.

DC potential shifts are the shifts observed in the EEG baseline which can last from seconds to minutes. The significance of these low-frequency components in healthy as well as pathological states of human physiology is getting more and more attention not only in scientific research but also in clinical applications. In this paper, we present our novel multimodal measurement setup for simultaneously investigating DC potential shifts in EEG (DC-EEG) and the changes in noninvasive transcutaneous pCO2 measurements. We present preliminary results of our measurements during hyperventilation and apnea, which are two commonly used activation methods for changes in pCO2.

Due to the direct contact between electrode and scalp, dry EEG electrodes are exposed to increased mechanical wear compared to conventional gel-based electrodes. However, state-of-the-art commercial cap systems commonly use permanently fixated electrodes which can lead to downtime of the EEG cap during professional repair and replacement as well as reduced overall lifetime. An easily replaceable EEG electrode would furthermore improve hygiene, especially for newborn and infant applications. We propose a novel replaceable electrode system, consisting of an electrode holder, a snap top, a contact ring fixated inside the electrode holder, and a replaceable electrode. The production process consists of 3D printing, silicone molding, resin casting, and electroless plating. The replaceable electrode system is integrated into a multichannel EEG cap system. A verification study is conducted with 30 volunteers. The operators experienced that the new electrode holder eases adjustment of the electrode to have proper contact with the scalp. During the study, defective electrodes can be replaced without a soldering process. Furthermore, all electrodes stayed in the holder and did not fall off the cap for the whole session. In conclusion, the novel replaceable electrode system is suitable for EEG measurements.

Self-care and improving one’s well-being has been growing rapidly in recent years for manifold reasons (e.g. higher workload, corona pandemic). Consumer-grade noninvasive stimulation devices are therefore on the rise to counteract the occurrence of mood disorders and burn-out symptoms. Here, we aim at investigating the impact of dynamically varying auditory-visual stimulation patterns on neural entrainment patterns and resonance phenomena. Twenty-two healthy volunteers (11 female, 25.4±5.1 years, one dropout, seven in control group) participated in the study. EEG data (64 channel; equidistant layout) were acquired preand during stimulation for each volunteer. Visual and auditory stimuli were presented via a headset (ATUM, Neuro- Bright; https://www.neurobright.co.uk/). Presentation patterns (frequency, intensity, spatial distribution) varied within a presentation session but were kept constant across all volunteers. Stimulus intensity was adjusted to individual comfort levels. Individual alpha peak frequencies (iAPF) were calculated via the power spectral density with 50% overlapping 10s epochs from pre-stimulation segments. For both, the study and the control group, a time-frequency representation was calculated for the pre- and during-stimulation segments. From this, power values were determined for different frequency-bands (iAPF, stimulation frequencies and second harmonics of the latter). Statistical analyses focused on contrasting the power values between pre- and during stimulation. Mean iAPF values were 10.25±0.99Hz for the study and 10.63±1.21Hz for the control group respectively. Both, power values at the stimulation frequencies and their second harmonics differed significantly between pre- and during stimulation (pstim=0.001; pharm=0.001) in the study group. No such difference was found for the control group (pstim=0.352; pharm=0.237). Further, neither the study nor the control group showed significant iAPF power differences (pstudy=0.035; pcontrol=0.352; alpha*=0.008). Our results suggest that lightweight, portable auditory-visual presentation devices represent an effective tool for generating entrainment and resonance effects at home. Further analyses will focus on the investigation of individual differences driving such modulatory effects.

Malignant melanoma is a very aggressive tumour with the ability to metastasize at an early stage. Therefore, early detection is of great importance. Multiphoton tomography is a new non-invasive examination method in the clinical diagnosis of skin alterations that can be used for such early diagnosis. In this paper, a method for automated evaluation of multiphoton images of the skin is presented. The following features at the cellular and subcellular level were extracted to differentiate between malignant melanomas, lesions, and healthy skin: cell symmetry, cell distance, cell density, cell and nucleus contrast, nucleus cell ratio, and homogeneity of cytoplasm. The extracted features formed the basis for the subsequent classification. Two feature sets were used. The first feature set included all the above-mentioned features, while the second feature set included the significantly different features between the three classes resulting from a multivariate analysis of variance. The classification was performed by a Support Vector Machine, the k-Nearest Neighbour algorithm, and Ensemble Learning. The best classification results were obtained with the Support Vector Machine using the first feature set with an accuracy of 52 % and 79.6 % for malignant melanoma and healthy skin, respectively. Despite the small number of subjects investigated our results indicate that the proposed automatic method can differentiate malignant melanoma, lesions, and healthy skin. For future clinical application, an extended study with more multiphoton images is needed.

Electroencephalography (EEG) is widely used in clinical applications and basic research. Dry EEG opened the application area to new fields like self-application during gaming and neurofeedback. While recording, the signals are always affected by artefacts. Manual detection of bad channels is the gold standard in both gel-based and dry EEG but is timeconsuming. We propose a simple and robust method for automatic bad channel detection in EEG. Our method is based on the iterative calculation of standard deviations for each channel. Statistical measures of these standard deviations serve as indications for bad channel detection. We compare the new method to the results obtained from the manually identified bad channels for EEG recordings. We analysed EEG signals during resting state with eyes closed and datasets with head movement. The results showed an accuracy of 99.69 % for both gel-based and dry EEG for resting state EEG. The accuracy of our new method is 99.38 % for datasets with the head movement for both setups. There was no significant difference between the manual gold standard of bad channel identification and our iterative standard deviation method. Therefore, the proposed iterative standard deviation method can be used for bad channel detection in resting state and movement EEG recordings.

Acoustic or visual warning signals for workers in hazardous situations might fail under loud and/or lowvisibility work situations. A warning system that uses electrocutaneous stimulation can overcome this problem. The aim of this pilot study was to find spatio-temporal stimulation patterns for appropriate electrical warning. Eight electrode pairs were attached to the upper right arm of 16 participants. The stimulation was conducted with bi-phasic rectangular pulses of 150 μs and an amplitude of up to 25 mA. Pulse intervals that generate a single pulse, pulsating, vibrating, and continuous perception as well as varying spatial patterns (e.g. alternating between electrode pairs or circumferentially around the arm) were investigated and evaluated with regard to alertness, discomfort, and urgency. The pilot study revealed that a stimulation signal that generates a vibrating perception and is applied as a circumferential signal around the arm showed the highest values of alertness and is therefore considered a potential warning pattern for future studies with larger study groups.

Warning of workers in dangerous situations is crucial. With the aim of deriving practical parameters for an electrocutaneous warning stimulation, we explore the design dimensions of pulse intervals, amplitudes, and locations of electrocutaneous stimulation in a study on healthy volunteers. Using biphasic rectangular current pulses on the upper right arm of 81 healthy participants, they evaluated temporal perception with varying intervals, ranging from 200 ms down to 0.5 ms, categorizing it as 'Individual pulses', 'Pulsating', 'Vibrating', or 'Continuous'. Next, we tested nine amplitude levels. Participants rated the perceived amplitude on a scale from 1 to 9 after a training phase. Finally, we presented five consecutive stimulation pulses in a pseudo-random order at eight electrode pair positions, asking participants to report the stimulated electrode pair. Participants perceived electrocutaneous pulses as 'Individual pulses' for median intervals above 74 ms, as 'Pulsating' between 44 ms and 74 ms, as 'Vibrating' between 12 ms and 44 ms, and as 'Continuous' below 12 ms. Pulse intervals below about 1 ms were perceived as weak and at about 5 ms as inconvenient, rendering these intervals less suitable for the design of a warning pattern. The median reported amplitudes [25%-75%-percentile] for presented amplitudes 1 to 9 are: 1[1–1], 2[2–3], 3[2–4], 3[3–4], 4[3–5], 5[4–6], 6[4–7], 7[5–8] and 7.5 [6–8] indicating a linear relationship between presented and perceived amplitude. These results suggest that the stimulation amplitude may be incorporated into a structured stimulation pattern. The majority of the electrode pair locations were reported correctly (64.3%–86.6%) or within the two neighboring electrode pairs (98%–99.7%). We conclude that the determined pulse intervals combined with the differentiability of locations offer the basis for designing a warning signal. Our research lays the groundwork for developing suitable signals for wearable electric warning devices. [ABSTRACT FROM AUTHOR]

Electroencephalographic (EEG) source localization is a fundamental tool for clinical diagnoses and brain-computer interfaces. We investigate the impact of model complexity on reconstruction accuracy by comparing the widely used three-layer boundary element method (BEM) as an inverse method against a five-layer BEM accelerated by the fast multipole method (BEM-FMM) and coupled with adaptive mesh refinement (AMR) as forward solver. Modern BEM-FMM with AMR can solve high-resolution multi-tissue models efficiently and accurately. We generated noiseless 256-channel EEG data from 15 subjects in the Connectome Young Adult dataset, using four anatomically relevant dipole positions, three conductivity sets, and two head segmentations; we mapped localization errors across the entire grey matter from 4000 dipole positions. The average location error among our four selected dipoles is ∼5 m m (±2 m m) with an orientation error of ∼ 12 ∘ (± 7 ∘ ). The average source localization error across the entire grey matter is ∼9 m m (±4 m m), with a tendency for smaller errors on the occipital lobe. Our findings indicate that while three-layer models are robust under noiseless conditions, substantial localization errors (10–20 m m) are common. Therefore, models of five or more layers may be needed for accurate source reconstruction in critical applications involving noisy EEG data. [ABSTRACT FROM AUTHOR]

Dry electrodes enable a shorter preparation time for infant EEG. Since infant skin is more sensitive than adult skin, soft electrodes are required to reduce the mechanical stress for this sensitive skin. Thus, soft electrodes are crucial for eventual repetitive and long-term use like in neonatal intensive care units. A biocompatible polyurethane (PU) can be produced in low hardness resulting in a soft and flexible electrode substrate. Silver/silver chloride (Ag/AgCl) electroless plating provides a conductive, electrochemically stable coating but the process may alter the mechanical properties of the electrode substrate. In this study, we assess the hardness of PU material before and after Ag/AgCl plating. The test sample design for Shore hardness measurement is based on ISO 7619-1:2010. Sample production consists of a 3D print master model, silicone molding, PU casting, and finally electroless plating. UPX 8400-1 (Sika AG, Switzerland) is used for the sample substrates. Test samples are produced with 7 different Shore hardness (range A40-A95) and 14 samples (each hardness: 1 uncoated and 1 coated). The hardness measurements are carried out with a lever-operated test stand Shore hardness tester model with a digital hardness tester (TI-AC with HDA 100-1, KERN &SOHN GmbH, Germany).. It is shown that there is a hardness increase (Shore A) due to Ag/AgCl coating with a grand average of 1.1±0.7 (p

Electrical stimulation (ES) techniques, such as deep brain and transcranial electrical stimulation, have shown promise in alleviating the symptoms of depression and other neurological disorders in vivo. A new noninvasive ES method called temporal interference stimulation (TIS), possesses great potential as it can be used to steer the stimulation and possibly selectively modulate different brain regions. To study TIS in a controlled environment, we successfully established an in vitro 'TIS on a chip' setup using rat cortical neurons on microelectrode arrays (MEAs) in combination with a current stimulator. We validated the developed TIS system and demonstrated the spatial steerability of the stimulation by direct electric field measurements in the chip setup. We stimulated cultures of rat cortical neurons at 28 days in vitro (DIV) by two-channel stimulation delivering 1) TIS at 653 Hz and 643 Hz, resulting in a 10 Hz frequency envelope, 2) low-frequency stimulation (LFS) at 10 Hz and 3) high-frequency stimulation (HFS) at 653 Hz. Unstimulated cultures were used as control/sham. We observed the differences in the electric field strengths during TIS, HFS, and LFS. Moreover, HFS and LFS had the smallest effects on neuronal activity. Instead, TIS elicited neuronal electrophysiological responses, especially 24 hours after stimulation. Our 'TIS on a chip' approach eludicates the applicability of TIS as a method to modulate neuronal electrophysiological activity. The TIS on a chip approach provides spatially steerable stimuli while mitigating the effects of high stimulus fields near the stimulation electrodes. Thus, the approach opens new avenues for stimulation on a chip applications, allowing the study of neuronal responses to gain insights into the potential clinical applications of TIS in treating various brain disorders. [ABSTRACT FROM AUTHOR]

Monitoring brain activity at home using electroencephalography (EEG) is an increasing trend for both medical and non-medical applications. Gel-based electrodes are not suitable due to the gel application requiring extensive preparation and cleaning support for the patient or user. Dry electrodes can be applied without prior preparation by the patient or user. We investigate and compare two dry electrode headbands for EEG acquisition: a novel hybrid dual-textile headband comprising multipin and multiwave electrodes and a neoprene-based headband comprising hydrogel and spidershaped electrodes. We compare the headbands and electrodes in terms of electrode-skin impedance, comfort, electrode offset potential and EEG signal quality. We did not observe considerable differences in the power spectral density of EEG recordings. However, the hydrogel electrodes showed considerably increased impedances and offset potentials, limiting their compatibility with many EEG amplifiers. The hydrogel and spider-shaped electrodes required increased adduction, resulting in a lower wearing comfort throughout the application time compared to the novel headband comprising multipin and multiwave electrodes.

Acoustic or visual warning signals for workers in hazardous situations might fail under loud and/or lowvisibility work situations. A warning system should be developed that uses electrocutaneous stimulation through textile electrodes. Previous work investigated suitable stimulation parameters using TENS electrodes. The aim of this study was to compare TENS and textile cuff electrodes in terms of sensation thresholds, qualitative and spatial sensation. A study on 30 healthy volunteers (f=13, m=17) of mean age of 26.7 years was conducted applying bi-phasic rectangular current pulses to electrodes attached to the upper right arm. The study revealed that perception, attention and intolerance thresholds, qualitative and spatial perception are comparable indicating that future studies with the textile cuff electrodes can be generally based on the previous results with TENS electrodes.

In this study a physiological closed-loop system for arterial CO2 partial pressure control was designed and comprehensively tested using a set of models of the respiratory CO2 gas exchange. The underlying preclinical data were collected from 12 pigs in presence of severe changes in hemodynamic and pulmonary condition. A minimally complex nonlinear state space model of CO2 gas exchange was identified post hoc in different lung conditions. The control variable was measured noninvasively using the endtidal CO2 partial pressure. For the simulation study the output signal of the controller was defined as the alveolar minute volume set value of an underlying adaptive lung protective ventilation mode. A linearisation of the two-compartment CO2 gas exchange model was used for the design of a model predictive controller (MPC). It was augmented by a tube based controller suppressing prediction errors due to model uncertainties. The controller was subject to comparative testing in interaction with each of the CO2 gas exchange models previously identified on the preclinical study data. The performance was evaluated for the system response towards the following five tests in comparison to a PID controller: recruitment maneuver, PEEP titration maneuver, stepwise change in the CO2 production, breath-hold maneuver and a step in the reference signal. A root mean square error of 2.69 mmHg between arterial CO2 partial pressure and the reference signal was achieved throughout the trial. The reference-variable response of the model predictive controller was superior regarding overshoot and settling time.