Accuracy of dipole source reconstruction in the 3-layer BEM model against the 5-layer BEM-FMM model.

Objective: To compare cortical dipole fitting spatial accuracy between the widely used yet highly simplified 3-layer and modern more realistic 5-layer BEM-FMM models with and without adaptive mesh refinement (AMR) methods. Methods: We generate simulated noiseless 256-channel EEG data from 5-layer (7-compartment) meshes of 15 subjects from the Connectome Young Adult dataset. For each subject, we test four dipole positions, three sets of conductivity values, and two types of head segmentation. We use the boundary element method (BEM) with fast multipole method (FMM) acceleration, with or without (AMR), for forward modeling. Dipole fitting is carried out with the FieldTrip MATLAB toolbox. Results: The average position error (across all tested dipoles, subjects, and models) is ~4 mm, with a standard deviation of ~2 mm. The orientation error is ~20° on average, with a standard deviation of ~15°. Without AMR, the numerical inaccuracies produce a larger disagreement between the 3- and 5-layer models, with an average position error of ~8 mm (6 mm standard deviation), and an orientation error of 28° (28° standard deviation). Conclusions: The low-resolution 3-layer models provide excellent accuracy in dipole localization. On the other hand, dipole orientation is retrieved less accurately. Therefore, certain applications may require more realistic models for practical source reconstruction. AMR is a critical component for improving the accuracy of forward EEG computations using a high-resolution 5-layer volume conduction model. Significance: Improving EEG source reconstruction accuracy is important for several clinical applications, including epilepsy and other seizure-inducing conditions.

Single-trial EEG analysis reveals burst structure during photic driving.

OBJECTIVE: Photic driving in the human visual cortex evoked by intermittent photic stimulation is usually characterized in averaged data by an ongoing oscillation showing frequency entrainment and resonance phenomena during the course of stimulation. We challenge this view of an ongoing oscillation by analyzing unaveraged data. METHODS: 64-channel EEGs were recorded during visual stimulation with light flashes at eight stimulation frequencies between 7.8 and 23 Hz for fourteen healthy volunteers. Time-frequency analyses were performed in averaged and unaveraged data. RESULTS: While we find ongoing oscillations in the averaged data during intermittent photic stimulation, we find transient events (bursts) of activity in the unaveraged data. Both resonance and entrainment occur for the ongoing oscillations in the averaged data and the bursts in the unaveraged data. CONCLUSIONS: We argue that the continuous oscillations in the averaged signal may be composed of brief, transient bursts in single trials. Our results can also explain previously observed amplitude fluctuations in averaged photic driving data. SIGNIFICANCE: Single-trial analyses might consequently improve our understanding of resonance and entrainment phenomena in the brain.

Rain may improve survival from direct lightning strikes to the human head.

There is evidence that humans can survive a direct lightning strike to the head. Our question is: could water (rain) on the skin contribute to an increase in the survival rate? We measure the influence of rain during high-energy direct lightning strikes on a realistic three-compartment human head phantom. We find a lower number of perforations and eroded areas near the lightning strike impact points on the head phantom when rain was applied compared to no rain. Current amplitudes in the brain were lower with rain compared to no rain before a fully formed flashover. We conclude that rain on the scalp potentially contributes to the survival rate of 70-90% due to: (1) lower current exposition in the brain before a fully formed flashover, and (2) reduced mechanical and thermal damage.

Entrainment and resonance effects with a new mobile audio-visual stimulation device.

Entrainment and photic driving effects triggered by repetitive visual stimulation are long-established in clinical and therapeutic scenarios. Nonetheless, such stimulation patterns are typically bound to stationary clinical and laboratory applications. We investigated the effects of repetitive stimulation with a new dynamic auditory-visual stimulation pattern using a novel consumer-grade stimulation device for home application.Fourteen volunteers (study group) received 16 sessions of combined auditory-visual stimulation during four weeks. An additional control group (seven volunteers) received auditory-only stimulation for two sessions. From 64-channel electroencephalography recordings, we compared individual alpha peak frequencies (iAPF) between week one and week four as well as power values from the time-frequency analysis.The novel stimulation device yielded stable entrainment and resonance effects for all investigated stimulation frequencies. Both groups showed no differences in their iAPFs between weeks one and four. The power comparison suggests that there are similar entrainment and resonance effects in week one and week four within the study group.We conclude that the novel portable consumer-grade stimulation device is suitable for home-based auditory-visual stimulation leading to consistent entrainment and resonance effects over the course of 16 stimulation sessions over four weeks.

Linear versus Quadratic Detrending in Analyzing Simultaneous Changes in DC-EEG and Transcutaneous pCO2.

Physiological direct current (DC) potential shifts in electroencephalography (EEG) can be masked by artifacts such as slow electrode drifts. To reduce the influence of these artifacts, linear detrending has been proposed as a pre-processing step. We considered quadratic detrending, which has hardly been addressed for ultralow frequency components in EEG. We compared the performance of linear and quadratic detrending in simultaneously acquired DC-EEG and transcutaneous partial pressure of carbon dioxide during two activation methods: hyperventilation (HV) and apnea (AP). Quadratic detrending performed significantly better than linear detrending in HV, while for AP, our analysis was inconclusive with no statistical significance. We conclude that quadratic detrending should be considered for DC-EEG preprocessing.

Simultaneous Dry and Gel-Based High-Density Electroencephalography Recordings.

Evaluations of new dry, high-density EEG caps have only been performed so far with serial measurements and not with simultaneous (parallel) measurements. For a first comparison of gel-based and dry electrode performance in simultaneous high-density EEG measurements, we developed a new EEG cap comprising 64 gel-based and 64 dry electrodes and performed simultaneous measurements on ten volunteers. We analyzed electrode-skin impedances, resting state EEG, triggered eye blinks, and visual evoked potentials (VEPs). To overcome the issue of different electrode positions in the comparison of simultaneous measurements, we performed spatial frequency analysis of the simultaneously measured EEGs using spatial harmonic analysis (SPHARA). The impedances were 516 ± 429 kOhm (mean ± std) for the dry electrodes and 14 ± 8 kOhm for the gel-based electrodes. For the dry EEG electrodes, we obtained a channel reliability of 77%. We observed no differences between dry and gel-based recordings for the alpha peak frequency and the alpha power amplitude, as well as for the VEP peak amplitudes and latencies. For the VEP, the RMSD and the correlation coefficient between the gel-based and dry recordings were 1.7 ± 0.7 µV and 0.97 ± 0.03, respectively. We observed no differences in the cumulative power distributions of the spatial frequency components for the N75 and P100 VEP peaks. The differences for the N145 VEP peak were attributed to the different noise characteristics of gel-based and dry recordings. In conclusion, we provide evidence for the equivalence of simultaneous dry and gel-based high-density EEG measurements.

Modeling the effects of transcranial magnetic stimulation on spatial attention.

Objectives. Transcranial magnetic stimulation (TMS) has been widely used to modulate brain activity in healthy and diseased brains, but the underlying mechanisms are not fully understood. Previous research leveraged biophysical modeling of the induced electric field (E-field) to map causal structure-function relationships in the primary motor cortex. This study aims at transferring this localization approach to spatial attention, which helps to understand the TMS effects on cognitive functions, and may ultimately optimize stimulation schemes.Approach. Thirty right-handed healthy participants underwent a functional magnetic imaging (fMRI) experiment, and seventeen of them participated in a TMS experiment. The individual fMRI activation peak within the right inferior parietal lobule (rIPL) during a Posner-like attention task defined the center target for TMS. Thereafter, participants underwent 500 Posner task trials. During each trial, a 5-pulse burst of 10 Hz repetitive TMS (rTMS) was given over the rIPL to modulate attentional processing. The TMS-induced E-fields for every cortical target were correlated with the behavioral modulation to identify relevant cortical regions for attentional orientation and reorientation.Main results. We did not observe a robust correlation between E-field strength and behavioral outcomes, highlighting the challenges of transferring the localization method to cognitive functions with high neural response variability and complex network interactions. Nevertheless, TMS selectively inhibited attentional reorienting in five out of seventeen subjects, resulting in task-specific behavioral impairments. The BOLD-measured neuronal activity and TMS-evoked neuronal effects showed different patterns, which emphasizes the principal distinction between the neural activity being correlated with (or maybe even caused by) particular paradigms, and the activity of neural populations exerting a causal influence on the behavioral outcome.Significance. This study is the first to explore the mechanisms of TMS-induced attentional modulation through electrical field modeling. Our findings highlight the complexity of cognitive functions and provide a basis for optimizing attentional stimulation protocols.

Flower electrodes for comfortable dry electroencephalography.

Dry electroencephalography (EEG) electrodes provide rapid, gel-free, and easy EEG preparation, but with limited wearing comfort. We propose a novel dry electrode comprising multiple tilted pins in a flower-like arrangement. The novel Flower electrode increases wearing comfort and contact area while maintaining ease of use. In a study with 20 volunteers, we compare the performance of a novel 64-channel dry Flower electrode cap to a commercial dry Multipin electrode cap in sitting and supine positions. The wearing comfort of the Flower cap was rated as significantly improved both in sitting and supine positions. The channel reliability and average impedances of both electrode systems were comparable. Averaged VEP components showed no considerable differences in global field power amplitude and latency, as well as in signal-to-noise ratio and topography. No considerable differences were found in the power spectral density of the resting state EEGs between 1 and 40 Hz. Overall, our findings provide evidence for equivalent channel reliability and signal characteristics of the compared cap systems in the sitting and supine positions. The reliability, signal quality, and significantly improved wearing comfort of the Flower electrode allow new fields of applications for dry EEG in long-term monitoring, sensitive populations, and recording in supine position.

Sex-related patterns in the electroencephalogram and their relevance in machine learning classifiers.

Deep learning is increasingly being proposed for detecting neurological and psychiatric diseases from electroencephalogram (EEG) data but the method is prone to inadvertently incorporate biases from training data and exploit illegitimate patterns. The recent demonstration that deep learning can detect the sex from EEG implies potential sex-related biases in deep learning-based disease detectors for the many diseases with unequal prevalence between males and females. In this work, we present the male- and female-typical patterns used by a convolutional neural network that detects the sex from clinical EEG (81% accuracy in a separate test set with 142 patients). We considered neural sources, anatomical differences, and non-neural artifacts as sources of differences in the EEG curves. Using EEGs from 1140 patients, we found electrocardiac artifacts to be leaking into the supposedly brain activity-based classifiers. Nevertheless, the sex remained detectable after rejecting heart-related and other artifacts. In the cleaned data, EEG topographies were critical to detect the sex, but waveforms and frequencies were not. None of the traditional frequency bands was particularly important for sex detection. We were able to determine the sex even from EEGs with shuffled time points and therewith completely destroyed waveforms. Researchers should consider neural and non-neural sources as potential origins of sex differences in their data, they should maintain best practices of artifact rejection, even when datasets are large, and they should test their classifiers for sex biases.

Spatiotemporal phase slip patterns for visual evoked potentials, covert object naming tasks, and insight moments extracted from 256 channel EEG recordings.

Phase slips arise from state transitions of the coordinated activity of cortical neurons which can be extracted from the EEG data. The phase slip rates (PSRs) were studied from the high-density (256 channel) EEG data, sampled at 16.384 kHz, of five adult subjects during covert visual object naming tasks. Artifact-free data from 29 trials were averaged for each subject. The analysis was performed to look for phase slips in the theta (4-7 Hz), alpha (7-12 Hz), beta (12-30 Hz), and low gamma (30-49 Hz) bands. The phase was calculated with the Hilbert transform, then unwrapped and detrended to look for phase slip rates in a 1.0 ms wide stepping window with a step size of 0.06 ms. The spatiotemporal plots of the PSRs were made by using a montage layout of 256 equidistant electrode positions. The spatiotemporal profiles of EEG and PSRs during the stimulus and the first second of the post-stimulus period were examined in detail to study the visual evoked potentials and different stages of visual object recognition in the visual, language, and memory areas. It was found that the activity areas of PSRs were different as compared with EEG activity areas during the stimulus and post-stimulus periods. Different stages of the insight moments during the covert object naming tasks were examined from PSRs and it was found to be about 512 ± 21 ms for the 'Eureka' moment. Overall, these results indicate that information about the cortical phase transitions can be derived from the measured EEG data and can be used in a complementary fashion to study the cognitive behavior of the brain.

Effects of spaceflight on the EEG alpha power and functional connectivity.

Electroencephalography (EEG) can detect changes in cerebral activity during spaceflight. This study evaluates the effect of spaceflight on brain networks through analysis of the Default Mode Network (DMN)'s alpha frequency band power and functional connectivity (FC), and the persistence of these changes. Five astronauts' resting state EEGs under three conditions were analyzed (pre-flight, in-flight, and post-flight). DMN's alpha band power and FC were computed using eLORETA and phase-locking value. Eyes-opened (EO) and eyes-closed (EC) conditions were differentiated. We found a DMN alpha band power reduction during in-flight (EC: p < 0.001; EO: p < 0.05) and post-flight (EC: p < 0.001; EO: p < 0.01) when compared to pre-flight condition. FC strength decreased during in-flight (EC: p < 0.01; EO: p < 0.01) and post-flight (EC: ns; EO: p < 0.01) compared to pre-flight condition. The DMN alpha band power and FC strength reduction persisted until 20 days after landing. Spaceflight caused electrocerebral alterations that persisted after return to earth. Periodic assessment by EEG-derived DMN analysis has the potential to become a neurophysiologic marker of cerebral functional integrity during exploration missions to space.

A multicenter feasibility study on implementing a brief mindful breathing exercise into regular university courses.

Practicing mindfulness is associated with stress reduction and with positive effects in the context of learning and teaching. Although effects on student populations have been studied extensively, there are few studies implementing mindfulness exercises in university courses directly. For this reason, we aimed to investigate whether the use of a brief mindfulness exercise in regular university courses, guided by the lecturers, is feasible and has immediate effects on the students' mental states. We conducted a preregistered multicenter study with one observational arm, following an ABAB design. In total, N = 325 students from 19 different university courses were included at baseline and n = 101 students at post measurement. Students were recruited by N = 14 lecturers located in six different universities in Germany. Lecturers started their courses either by guiding a brief mindfulness exercise (intervention condition) or as they regularly would, with no such exercise (control condition). In both conditions, the mental states of students and lecturers were assessed. Over the semester, n = 1193 weekly observations from students and n = 160 observations from lecturers were collected. Intervention effects were analyzed with linear mixed-effects models. The brief mindfulness exercise, compared to no such exercise, was associated with lower stress composite scores, higher presence composite scores, higher motivation for the courses, as well as better mood in students. Effects persisted throughout a respective course session. Lecturers also reported positive effects of instructing mindfulness. Implementing a brief mindfulness exercise in regular university teaching sessions is feasible and has positive effects on both students and lecturers.

A systematic comparison of deep learning methods for EEG time series analysis.

Analyzing time series data like EEG or MEG is challenging due to noisy, high-dimensional, and patient-specific signals. Deep learning methods have been demonstrated to be superior in analyzing time series data compared to shallow learning methods which utilize handcrafted and often subjective features. Especially, recurrent deep neural networks (RNN) are considered suitable to analyze such continuous data. However, previous studies show that they are computationally expensive and difficult to train. In contrast, feed-forward networks (FFN) have previously mostly been considered in combination with hand-crafted and problem-specific feature extractions, such as short time Fourier and discrete wavelet transform. A sought-after are easily applicable methods that efficiently analyze raw data to remove the need for problem-specific adaptations. In this work, we systematically compare RNN and FFN topologies as well as advanced architectural concepts on multiple datasets with the same data preprocessing pipeline. We examine the behavior of those approaches to provide an update and guideline for researchers who deal with automated analysis of EEG time series data. To ensure that the results are meaningful, it is important to compare the presented approaches while keeping the same experimental setup, which to our knowledge was never done before. This paper is a first step toward a fairer comparison of different methodologies with EEG time series data. Our results indicate that a recurrent LSTM architecture with attention performs best on less complex tasks, while the temporal convolutional network (TCN) outperforms all the recurrent architectures on the most complex dataset yielding a 8.61% accuracy improvement. In general, we found the attention mechanism to substantially improve classification results of RNNs. Toward a light-weight and online learning-ready approach, we found extreme learning machines (ELM) to yield comparable results for the less complex tasks.

Three-Dimensional Immersion Scanning Technique: A Scalable Low-Cost Solution for 3D Scanning Using Water-Based Fluid.

Three-dimensional scanning technology has been traditionally used in the medical and engineering industries, but these scanners can be expensive or limited in their capabilities. This research aimed to develop low-cost 3D scanning using rotation and immersion in a water-based fluid. This technique uses a reconstruction approach similar to CT scanners but with significantly less instrumentation and cost than traditional CT scanners or other optical scanning techniques. The setup consisted of a container filled with a mixture of water and Xanthan gum. The object to be scanned was submerged at various rotation angles. A stepper motor slide with a needle was used to measure the fluid level increment as the object being scanned was submerged into the container. The results showed that the 3D scanning using immersion in a water-based fluid was feasible and could be adapted to a wide range of object sizes. The technique produced reconstructed images of objects with gaps or irregularly shaped openings in a low-cost fashion. A 3D printed model with a width of 30.7200 ± 0.2388 mm and height of 31.6800 ± 0.3445 mm was compared to its scan to evaluate the precision of the technique. Its width/height ratio (0.9697 ± 0.0084) overlaps the margin of error of the width/height ratio of the reconstructed image (0.9649 ± 0.0191), showing statistical similarities. The signal-to-noise ratio was calculated at around 6 dB. Suggestions for future work are made to improve the parameters of this promising, low-cost technique.

Transferability of cathodal tDCS effects from the primary motor to the prefrontal cortex: A multimodal TMS-EEG study.

Neurophysiological effects of transcranial direct current stimulation (tDCS) have been extensively studied over the primary motor cortex (M1). Much less is however known about its effects over non-motor areas, such as the prefrontal cortex (PFC), which is the neuronal foundation for many high-level cognitive functions and involved in neuropsychiatric disorders. In this study, we, therefore, explored the transferability of cathodal tDCS effects over M1 to the PFC. Eighteen healthy human participants (11 males and 8 females) were involved in eight randomized sessions per participant, in which four cathodal tDCS dosages, low, medium, and high, as well as sham stimulation, were applied over the left M1 and left PFC. After-effects of tDCS were evaluated via transcranial magnetic stimulation (TMS)-electroencephalography (EEG), and TMS-elicited motor evoked potentials (MEP), for the outcome parameters TMS-evoked potentials (TEP), TMS-evoked oscillations, and MEP amplitude alterations. TEPs were studied both at the regional and global scalp levels. The results indicate a regional dosage-dependent nonlinear neurophysiological effect of M1 tDCS, which is not one-to-one transferable to PFC tDCS. Low and high dosages of M1 tDCS reduced early positive TEP peaks (P30, P60), and MEP amplitudes, while an enhancement was observed for medium dosage M1 tDCS (P30). In contrast, prefrontal low, medium and high dosage tDCS uniformly reduced the early positive TEP peak amplitudes. Furthermore, for both cortical areas, regional tDCS-induced modulatory effects were not observed for late TEP peaks, nor TMS-evoked oscillations. However, at the global scalp level, widespread effects of tDCS were observed for both, TMS-evoked potentials and oscillations. This study provides the first direct physiological comparison of tDCS effects applied over different brain areas and therefore delivers crucial information for future tDCS applications.

Noise characteristics in spaceflight multichannel EEG.

The cognitive performance of the crew has a major impact on mission safety and success in space flight. Monitoring of cognitive performance during long-duration space flight therefore is of paramount importance and can be performed using compact state-of-the-art mobile EEG. However, signal quality of EEG may be compromised due to the vicinity to various electronic devices and constant movements. We compare noise characteristics between in-flight extraterrestrial microgravity and ground-level terrestrial electroencephalography (EEG) recordings. EEG data recordings from either aboard International Space Station (ISS) or on earth's surface, utilizing three EEG amplifiers and two electrode types, were compared. In-flight recordings showed noise level of an order of magnitude lower when compared to pre- and post-flight ground-level recordings with the same EEG system. Noise levels between ground-level recordings with actively shielded cables, and in-flight recordings without shielded cables, were similar. Furthermore, noise level characteristics of shielded ground-level EEG recordings, using wet and dry electrodes, and in-flight EEG recordings were similar. Actively shielded mobile dry EEG systems will support neuroscientific research and neurocognitive monitoring during spaceflight, especially during long-duration space missions.

Review of individualized current flow modeling studies for transcranial electrical stimulation.

There is substantial intersubject variability of behavioral and neurophysiological responses to transcranial electrical stimulation (tES), which represents one of the most important limitations of tES. Many tES protocols utilize a fixed experimental parameter set disregarding individual anatomical and physiological properties. This one-size-fits-all approach might be one reason for the observed interindividual response variability. Simulation of current flow applying head models based on available anatomical data can help to individualize stimulation parameters and contribute to the understanding of the causes of this response variability. Current flow modeling can be used to retrospectively investigate the characteristics of tES effectivity. Previous studies examined, for example, the impact of skull defects and lesions on the modulation of current flow and demonstrated effective stimulation intensities in different age groups. Furthermore, uncertainty analysis of electrical conductivities in current flow modeling indicated the most influential tissue compartments. Current flow modeling, when used in prospective study planning, can potentially guide stimulation configurations resulting in individually effective tES. Specifically, current flow modeling using individual or matched head models can be employed by clinicians and scientists to, for example, plan dosage in tES protocols for individuals or groups of participants. We review studies that show a relationship between the presence of behavioral/neurophysiological responses and features derived from individualized current flow models. We highlight the potential benefits of individualized current flow modeling.

Precise motor mapping with transcranial magnetic stimulation.

We describe a routine to precisely localize cortical muscle representations within the primary motor cortex with transcranial magnetic stimulation (TMS) based on the functional relation between induced electric fields at the cortical level and peripheral muscle activation (motor-evoked potentials; MEPs). Besides providing insights into structure-function relationships, this routine lays the foundation for TMS dosing metrics based on subject-specific cortical electric field thresholds. MEPs for different coil positions and orientations are combined with electric field modeling, exploiting the causal nature of neuronal activation to pinpoint the cortical origin of the MEPs. This involves constructing an individual head model using magnetic resonance imaging, recording MEPs via electromyography during TMS and computing the induced electric fields with numerical modeling. The cortical muscle representations are determined by relating the TMS-induced electric fields to the MEP amplitudes. Subsequently, the coil position to optimally stimulate the origin of the identified cortical MEP can be determined by numerical modeling. The protocol requires 2 h of manual preparation, 10 h for the automated head model construction, one TMS session lasting 2 h, 12 h of computational postprocessing and an optional second TMS session lasting 30 min. A basic level of computer science expertise and standard TMS neuronavigation equipment suffices to perform the protocol.

Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps.

Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain-computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.