Scalable Anatomically-Tunable Fully In-Ear Dry-Electrode Array for User-Generic Unobtrusive Electrophysiology.

Traditional scalp EEG instrumentation is bulky and arduous to set up, requiring wires that constrain the subject's movement, conductive wet gels that dry over time which limits long-term recording, and/or is socially stigmatized. Therefore, there is growing research in in-ear EEG to increase user wearability, ease of use, and concealability. However, the fabrication of in-ear EEG sensors utilizes complex equipment and materials to capture the intricate geometry of the ear and to fabricate custom earpieces and electrodes. This work aims to lower the barrier of entry by decreasing the fabrication complexity by using PCB components with versatile, user-generic designs. Measured results on the assembled earpiece demonstrate that it viably captures eye blinks, jaw clench, auditory steady-state response (ASSR), and alpha modulation. Additionally, electrochemical impedance spectroscopy (EIS) experiments show reliable electrode-skin contact with impedance comparable to conventional dry-electrode designs at substantially greater channel density.

Motion and Muscle Artifact Removal Validation Using an Electrical Head Phantom, Robotic Motion Platform, and Dual Layer Mobile EEG.

Motion and muscle artifacts can undermine signal quality in electroencephalography (EEG) recordings during locomotion. We evaluated approaches for recovering ground-truth artificial brain signals from noisy EEG recordings. We built an electrical head phantom that broadcast four brain and four muscle sources. Head movements were generated by a robotic motion platform. We recorded 128-channel dual layer EEG and 8-channel neck electromyography (EMG) from the head phantom during motion. We evaluated ground-truth electrocortical source signal recovery from artifact contaminated data using Independent Component Analysis (ICA) to determine: (1) the number of isolated noise sensor recordings needed to capture and remove motion artifacts, (2) the ability of Artifact Subspace Reconstruction to remove motion and muscle artifacts at contrasting artifact detection thresholds, (3) the number of neck EMG sensor recordings needed to capture and remove muscle artifacts, and (4) the ability of Canonical Correlation Analysis to remove muscle artifacts. We also evaluated source signal recovery by combining the best practices identified in aims 1-4. By including isolated noise and EMG recordings in the ICA decomposition, we more effectively recovered ground-truth artificial brain signals. A reduced subset of 32-noise and 6-EMG channels showed equivalent performance compared to including the complete arrays. Artifact Subspace Reconstruction improved source separation, but this was contingent on muscle activity amplitude. Canonical Correlation Analysis also improved source separation. Merging noise and EMG recordings into the ICA decomposition, with Artifact Subspace Reconstruction and Canonical Correlation Analysis preprocessing, improved source signal recovery. This study expands on previous head phantom experiments by including neck muscle source activity and evaluating artificial electrocortical spectral power fluctuations synchronized with gait events.

Faster Gait Speeds Reduce Alpha and Beta EEG Spectral Power From Human Sensorimotor Cortex.

OBJECTIVE: Our aim was to determine if walking speed affected human sensorimotor electrocortical dynamics using mobile high-density electroencephalography (EEG). METHODS: To overcome limitations associated with motion and muscle artifact contamination in EEG recordings, we compared solutions for artifact removal using novel dual-layer EEG electrodes and alternative signal processing methods. Dual-layer EEG simultaneously recorded human electrocortical signals and isolated motion artifacts using pairs of mechanically coupled and electrically independent electrodes. For electrical muscle activity removal, we incorporated electromyographic (EMG) recordings from the neck into our mobile EEG data processing pipeline. We compared artifact removal methods during treadmill walking at four speeds (0.5, 1.0, 1.5, and 2.0 m/s). RESULTS: Left and right sensorimotor alpha and beta spectral power increased in contralateral limb single support and push off, and decreased during contralateral limb swing at each speed. At faster walking speeds, sensorimotor spectral power fluctuations were less pronounced across the gait cycle with reduced alpha and beta power (p < 0.05) compared to slower speeds. Isolated noise recordings and neck EMG spectral power fluctuations matched gait events and showed broadband spectral power increases at faster speeds. CONCLUSION AND SIGNIFICANCE: Dual-layer EEG enabled us to isolate changes in human sensorimotor electrocortical dynamics across walking speeds. A comparison of signal processing approaches revealed similar intrastride cortical fluctuations when applying common (e.g., artifact subspace reconstruction) and novel artifact rejection methods. Dual-layer EEG, however, allowed us to document and rule out residual artifacts, which exposed sensorimotor spectral power changes across gait speeds.

Human electrocortical dynamics while stepping over obstacles.

To better understand human brain dynamics during visually guided locomotion, we developed a method of removing motion artifacts from mobile electroencephalography (EEG) and studied human subjects walking and running over obstacles on a treadmill. We constructed a novel dual-layer EEG electrode system to isolate electrocortical signals, and then validated the system using an electrical head phantom and robotic motion platform. We collected data from young healthy subjects walking and running on a treadmill while they encountered unexpected obstacles to step over. Supplementary motor area and premotor cortex had spectral power increases within ~200 ms after object appearance in delta, theta, and alpha frequency bands (3-13 Hz). That activity was followed by similar posterior parietal cortex spectral power increase that decreased in lag time with increasing locomotion speed. The sequence of activation suggests that supplementary motor area and premotor cortex interrupted the gait cycle, while posterior parietal cortex tracked obstacle location for planning foot placement nearly two steps ahead of reaching the obstacle. Together, these results highlight advantages of adopting dual-layer mobile EEG, which should greatly facilitate the study of human brain dynamics in physically active real-world settings and tasks.

Hardware Implementation of an Adaptive Data Acquisition System for Real-World EEG.

Collecting EEG involves digitizing a very small signal across a vast potential dynamic range, particularly within real-world neuroimaging conditions, where noise can be especially prominent. Conventional methods require highresolution, power-hungry data acquisition systems (DAQs), creating limits on usable time before manual interaction is necessary for recharge. Here, we discuss continued work on an alternative DAQ approach capable of acquiring high resolution data with ultra-low power use by adjusting parameters of the analog front end (AFE) in real time to allow use of low-resolution ADCs. This work compares signal quality of a hardware implementation of our adaptive AFE DAQ to that of an industry standard DAQ. Results demonstrate successful reconstruction of signals in both clean and noisy EEG monitoring environments at low bit-depths while maintaining high correlation and low standard deviation of error. This suggests promise for a fully integrated implementation with substantially lower power consumption.

Dual-electrode motion artifact cancellation for mobile electroencephalography.

OBJECTIVE: Our purpose was to evaluate the ability of a dual electrode approach to remove motion artifact from electroencephalography (EEG) measurements. APPROACH: We used a phantom human head model and robotic motion platform to induce motion while collecting scalp EEG. We assembled a dual electrode array capturing (a) artificial neural signals plus noise from scalp EEG electrodes, and (b) electrically isolated motion artifact noise. We recorded artificial neural signals broadcast from antennae in the phantom head during continuous vertical sinusoidal movements (stationary, 1.00, 1.25, 1.50, 1.75, 2.00 Hz movement frequencies). We evaluated signal quality using signal-to-noise ratio (SNR), cross-correlation, and root mean square error (RMSE) between the ground truth broadcast signals and the recovered EEG signals. MAIN RESULTS: Signal quality was restored following noise cancellation when compared to single electrode EEG measurements collected with no phantom head motion. SIGNIFICANCE: We achieved substantial motion artifact attenuation using secondary electrodes for noise cancellation. These methods can be applied to studying electrocortical signals during human locomotion to improve real-world neuroimaging using EEG.

Effects of Cable Sway, Electrode Surface Area, and Electrode Mass on Electroencephalography Signal Quality during Motion.

More neuroscience researchers are using scalp electroencephalography (EEG) to measure electrocortical dynamics during human locomotion and other types of movement. Motion artifacts corrupt the EEG and mask underlying neural signals of interest. The cause of motion artifacts in EEG is often attributed to electrode motion relative to the skin, but few studies have examined EEG signals under head motion. In the current study, we tested how motion artifacts are affected by the overall mass and surface area of commercially available electrodes, as well as how cable sway contributes to motion artifacts. To provide a ground-truth signal, we used a gelatin head phantom with embedded antennas broadcasting electrical signals, and recorded EEG with a commercially available electrode system. A robotic platform moved the phantom head through sinusoidal displacements at different frequencies (0-2 Hz). Results showed that a larger electrode surface area can have a small but significant effect on improving EEG signal quality during motion and that cable sway is a major contributor to motion artifacts. These results have implications in the development of future hardware for mobile brain imaging with EEG.

Carbon nanofiber-filled conductive silicone elastomers as soft, dry bioelectronic interfaces.

Soft and pliable conductive polymer composites hold promise for application as bioelectronic interfaces such as for electroencephalography (EEG). In clinical, laboratory, and real-world EEG there is a desire for dry, soft, and comfortable interfaces to the scalp that are capable of relaying the µV-level scalp potentials to signal processing electronics. A key challenge is that most material approaches are sensitive to deformation-induced shifts in electrical impedance associated with decreased signal-to-noise ratio. This is a particular concern in real-world environments where human motion is present. The entire set of brain information outside of tightly controlled laboratory or clinical settings are currently unobtainable due to this challenge. Here we explore the performance of an elastomeric material solution purposefully designed for dry, soft, comfortable scalp contact electrodes for EEG that is specifically targeted to have flat electrical impedance response to deformation to enable utilization in real world environments. A conductive carbon nanofiber filled polydimethylsiloxane (CNF-PDMS) elastomer was evaluated at three fill ratios (3, 4 and 7 volume percent). Electromechanical testing data is presented showing the influence of large compressive deformations on electrical impedance as well as the impact of filler loading on the elastomer stiffness. To evaluate usability for EEG, pre-recorded human EEG signals were replayed through the contact electrodes subjected to quasi-static compressive strains between zero and 35%. These tests show that conductive filler ratios well above the electrical percolation threshold are desirable in order to maximize signal-to-noise ratio and signal correlation with an ideal baseline. Increasing fill ratios yield increasingly flat electrical impedance response to large applied compressive deformations with a trade in increased material stiffness, and with nominal electrical impedance tunable over greater than 4 orders of magnitude. EEG performance was independent of filler loading above 4 vol % CNF (< 103 ohms).

An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons.

This data note describes an 18-subject EEG (electroencephalogram) data collection from an experiment in which subjects performed a standard visual oddball task. Several research projects have used this data to test artifact detection, classification, transfer learning, EEG preprocessing, blink detection, and automated annotation algorithms. We are releasing the data in three formats to enable benchmarking of EEG algorithms in many areas. The data was acquired using a Biosemi Active 2 EEG headset and includes 64 channels of EEG, 4 channels of EOG (electrooculogram), and 2 mastoid reference channels.

EEG-Annotate: Automated identification and labeling of events in continuous signals with applications to EEG.

BACKGROUND: In controlled laboratory EEG experiments, researchers carefully mark events and analyze subject responses time-locked to these events. Unfortunately, such markers may not be available or may come with poor timing resolution for experiments conducted in less-controlled naturalistic environments. NEW METHOD: We present an integrated event-identification method for identifying particular responses that occur in unlabeled continuously recorded EEG signals based on information from recordings of other subjects potentially performing related tasks. We introduce the idea of timing slack and timing-tolerant performance measures to deal with jitter inherent in such non-time-locked systems. We have developed an implementation available as an open-source MATLAB toolbox (http://github.com/VisLab/EEG-Annotate) and have made test data available in a separate data note. RESULTS: We applied the method to identify visual presentation events (both target and non-target) in data from an unlabeled subject using labeled data from other subjects with good sensitivity and specificity. The method also identified actual visual presentation events in the data that were not previously marked in the experiment. COMPARISON WITH EXISTING METHODS: Although the method uses traditional classifiers for initial stages, the problem of identifying events based on the presence of stereotypical EEG responses is the converse of the traditional stimulus-response paradigm and has not been addressed in its current form. CONCLUSIONS: In addition to identifying potential events in unlabeled or incompletely labeled EEG, these methods also allow researchers to investigate whether particular stereotypical neural responses are present in other circumstances. Timing-tolerance has the added benefit of accommodating inter- and intra- subject timing variations.

Sustained Attention in Real Classroom Settings: An EEG Study.

Sustained attention is a process that enables the maintenance of response persistence and continuous effort over extended periods of time. Performing attention-related tasks in real life involves the need to ignore a variety of distractions and inhibit attention shifts to irrelevant activities. This study investigates electroencephalography (EEG) spectral changes during a sustained attention task within a real classroom environment. Eighteen healthy students were instructed to recognize as fast as possible special visual targets that were displayed during regular university lectures. Sorting their EEG spectra with respect to response times, which indicated the level of visual alertness to randomly introduced visual stimuli, revealed significant changes in the brain oscillation patterns. The results of power-frequency analysis demonstrated a relationship between variations in the EEG spectral dynamics and impaired performance in the sustained attention task. Across subjects and sessions, prolongation of the response time was preceded by an increase in the delta and theta EEG powers over the occipital region, and decrease in the beta power over the occipital and temporal regions. Meanwhile, implementation of the complex attention task paradigm into a real-world classroom setting makes it possible to investigate specific mutual links between brain activities and factors that cause impaired behavioral performance, such as development and manifestation of classroom mental fatigue. The findings of the study set a basis for developing a system capable of estimating the level of visual attention during real classroom activities by monitoring changes in the EEG spectra.

EEG correlates of sensorimotor processing: independent components involved in sensory and motor processing.

Sensorimotor processing is a critical function of the human brain with multiple cortical areas specialised for sensory recognition or motor execution. Although there has been considerable research into sensorimotor control in humans, the steps between sensory recognition and motor execution are not fully understood. To provide insight into brain areas responsible for sensorimotor computation, we used complex categorization-response tasks (variations of a Stroop task requiring recognition, decision-making, and motor responses) to test the hypothesis that some functional modules are participating in both sensory as well as motor processing. We operationalize functional modules as independent components (ICs) yielded by an independent component analysis (ICA) of EEG data and measured event-related responses by means of inter-trial coherence (ITC). Our results consistently found ICs with event-related ITC responses related to both sensory stimulation and motor response onsets (on average 5.8 ICs per session). These findings reveal EEG correlates of tightly coupled sensorimotor processing in the human brain, and support frameworks like embodied cognition, common coding, and sensorimotor contingency that do not sequentially separate sensory and motor brain processes.

Restricted vision increases sensorimotor cortex involvement in human walking.

This study aimed to determine whether there is electrocortical evidence of augmented participation of sensory brain areas in walking modulation during walking with eyes closed. Healthy subjects (n = 10) walked on a treadmill at 1 m/s while alternating 5 min of walking with the eyes open or closed while we recorded ground reaction forces (GRFs) and high-density scalp electroencephalography (EEG). We applied independent component analysis to parse EEG signals into maximally independent component (IC) processes and then computed equivalent current dipoles for each IC. We clustered cortical source ICs and analyzed event-related spectral perturbations synchronized to gait events. Our results indicated that walking with eyes closed reduced the first peak of the vertical GRFs and induced shorter stride duration. Regarding the EEG, we found that walking with eyes closed induced significantly increased relative theta desynchronization in the frontal and premotor cortex during stance, as well as greater desynchronization from theta to beta bands during transition to single support for both left and right somatosensory cortex. These results suggest a phase-specific increased participation of brain areas dedicated to sensory processing and integration when vision is not available for locomotor guidance. Furthermore, the lack of vision demands higher neural processing related to motor planning and execution. Our findings provide evidence supporting the use of eyes-closed tasks in clinical practice, such as gait rehabilitation and improvements in balance control, as there is higher demand for additional sensory integration for achieving postural control.NEW & NOTEWORTHY We measured electrocortical dynamics in sighted individuals while walking with eyes open and eyes closed to induce the participation of other sensory systems in postural control. Our findings show that walking with visual restriction increases the participation of brain areas dedicated to sensory processing, motor planning, and execution. These results confirm the essential participation of supraspinal inputs to postural control in human locomotion, supporting the use of eyes-closed tasks in clinical practice.

A Channel Rejection Method for Attenuating Motion-Related Artifacts in EEG Recordings during Walking.

Recording scalp electroencephalography (EEG) during human motion can introduce motion artifacts. Repetitive head movements can generate artifact patterns across scalp EEG sensors. There are many methods for identifying and rejecting bad channels and independent components from EEG datasets, but there is a lack of methods dedicated to evaluate specific intra-channel amplitude patterns for identifying motion-related artifacts. In this study, we proposed a template correlation rejection (TCR) as a novel method for identifying and rejecting EEG channels and independent components carrying motion-related artifacts. We recorded EEG data from 10 subjects during treadmill walking. The template correlation rejection method consists of creating templates of amplitude patterns and determining the fraction of total epochs presenting relevant correlation to the template. For EEG channels, the template correlation rejection removed channels presenting the majority of epochs (>75%) correlated to the template, and presenting pronounced amplitude in comparison to all recorded channels. For independent components, the template correlation rejection removed components presenting the majority of epochs correlated to the template. Evaluation of scalp maps and power spectra confirmed low neural content for the rejected components. We found that channels identified for rejection contained ~60% higher delta power, and had spectral properties locked to the gait phases. After rejecting the identified channels and running independent component analysis on the EEG datasets, the proposed method identified 4.3 ± 1.8 independent components (out of 198 ± 12) with substantive motion-related artifacts. These results indicate that template correlation rejection is an effective method for rejecting EEG channels contaminated with motion-related artifact during human locomotion.

Systems, Subjects, Sessions: To What Extent Do These Factors Influence EEG Data?

Lab-based electroencephalography (EEG) techniques have matured over decades of research and can produce high-quality scientific data. It is often assumed that the specific choice of EEG system has limited impact on the data and does not add variance to the results. However, many low cost and mobile EEG systems are now available, and there is some doubt as to the how EEG data vary across these newer systems. We sought to determine how variance across systems compares to variance across subjects or repeated sessions. We tested four EEG systems: two standard research-grade systems, one system designed for mobile use with dry electrodes, and an affordable mobile system with a lower channel count. We recorded four subjects three times with each of the four EEG systems. This setup allowed us to assess the influence of all three factors on the variance of data. Subjects performed a battery of six short standard EEG paradigms based on event-related potentials (ERPs) and steady-state visually evoked potential (SSVEP). Results demonstrated that subjects account for 32% of the variance, systems for 9% of the variance, and repeated sessions for each subject-system combination for 1% of the variance. In most lab-based EEG research, the number of subjects per study typically ranges from 10 to 20, and error of uncertainty in estimates of the mean (like ERP) will improve by the square root of the number of subjects. As a result, the variance due to EEG system (9%) is of the same order of magnitude as variance due to subjects (32%/sqrt(16) = 8%) with a pool of 16 subjects. The two standard research-grade EEG systems had no significantly different means from each other across all paradigms. However, the two other EEG systems demonstrated different mean values from one or both of the two standard research-grade EEG systems in at least half of the paradigms. In addition to providing specific estimates of the variability across EEG systems, subjects, and repeated sessions, we also propose a benchmark to evaluate new mobile EEG systems by means of ERP responses.

BLASST: Band Limited Atomic Sampling With Spectral Tuning With Applications to Utility Line Noise Filtering.

OBJECTIVE: In this paper, we present and test a new method for the identification and removal of nonstationary utility line noise from biomedical signals. METHODS: The method, band limited atomic sampling with spectral tuning (BLASST), is an iterative approach that is designed to 1) fit nonstationarities in line noise by searching for best-fit Gabor atoms at predetermined time points, 2) self-modulate its fit by leveraging information from frequencies surrounding the target frequency, and 3) terminate based on a convergence criterion obtained from the same surrounding frequencies. To evaluate the performance of the proposed algorithm, we generate several simulated and real instances of nonstationary line noise and test BLASST along with alternative filtering approaches. RESULTS: We find that BLASST is capable of fitting line noise well and/or preserving local signal features relative to tested alternative filtering techniques. CONCLUSION: BLASST may present a useful alternative to bandpass, notch, or other filtering methods when experimentally relevant features have significant power in a spectrum that is contaminated by utility line noise, or when the line noise in question is highly nonstationary. SIGNIFICANCE: This is of particular significance in electroencephalography experiments, where line noise may be present in the frequency bands of neurological interest and measurements are typically of low enough strength that induced line noise can dominate the recorded signals. In conjunction with this paper, the authors have released a MATLAB toolbox that performs BLASST on real, vector-valued signals (available at https://github.com/VisLab/blasst).

Pupil Sizes Scale with Attentional Load and Task Experience in a Multiple Object Tracking Task.

Previous studies have related changes in attentional load to pupil size modulations. However, studies relating changes in attentional load and task experience on a finer scale to pupil size modulations are scarce. Here, we investigated how these changes affect pupil sizes. To manipulate attentional load, participants covertly tracked between zero and five objects among several randomly moving objects on a computer screen. To investigate effects of task experience, the experiment was conducted on three consecutive days. We found that pupil sizes increased with each increment in attentional load. Across days, we found systematic pupil size reductions. We compared the model fit for predicting pupil size modulations using attentional load, task experience, and task performance as predictors. We found that a model which included attentional load and task experience as predictors had the best model fit while adding performance as a predictor to this model reduced the overall model fit. Overall, results suggest that pupillometry provides a viable metric for precisely assessing attentional load and task experience in visuospatial tasks.

New Flexible Silicone-Based EEG Dry Sensor Material Compositions Exhibiting Improvements in Lifespan, Conductivity, and Reliability.

This study investigates alternative material compositions for flexible silicone-based dry electroencephalography (EEG) electrodes to improve the performance lifespan while maintaining high-fidelity transmission of EEG signals. Electrode materials were fabricated with varying concentrations of silver-coated silica and silver flakes to evaluate their electrical, mechanical, and EEG transmission performance. Scanning electron microscope (SEM) analysis of the initial electrode development identified some weak points in the sensors' construction, including particle pull-out and ablation of the silver coating on the silica filler. The newly-developed sensor materials achieved significant improvement in EEG measurements while maintaining the advantages of previous silicone-based electrodes, including flexibility and non-toxicity. The experimental results indicated that the proposed electrodes maintained suitable performance even after exposure to temperature fluctuations, 85% relative humidity, and enhanced corrosion conditions demonstrating improvements in the environmental stability. Fabricated flat (forehead) and acicular (hairy sites) electrodes composed of the optimum identified formulation exhibited low impedance and reliable EEG measurement; some initial human experiments demonstrate the feasibility of using these silicone-based electrodes for typical lab data collection applications.

Oscillatory activity in auditory cortex reflects the perceptual level of audio-tactile integration.

Cross-modal interactions between sensory channels have been shown to depend on both the spatial disparity and the perceptual similarity between the presented stimuli. Here we investigate the behavioral and neural integration of auditory and tactile stimulus pairs at different levels of spatial disparity. Additionally, we modulated the amplitudes of both stimuli in either a coherent or non-coherent manner. We found that both auditory and tactile localization performance was biased towards the stimulus in the respective other modality. This bias linearly increases with stimulus disparity and is more pronounced for coherently modulated stimulus pairs. Analyses of electroencephalographic (EEG) activity at temporal-cortical sources revealed enhanced event-related potentials (ERPs) as well as decreased alpha and beta power during bimodal as compared to unimodal stimulation. However, while the observed ERP differences are similar for all stimulus combinations, the extent of oscillatory desynchronization varies with stimulus disparity. Moreover, when both stimuli were subjectively perceived as originating from the same direction, the reduction in alpha and beta power was significantly stronger. These observations suggest that in the EEG the level of perceptual integration is mainly reflected by changes in ongoing oscillatory activity.

Proposing Metrics for Benchmarking Novel EEG Technologies Towards Real-World Measurements.

Recent advances in electroencephalographic (EEG) acquisition allow for recordings using wet and dry sensors during whole-body motion. The large variety of commercially available EEG systems contrasts with the lack of established methods for objectively describing their performance during whole-body motion. Therefore, the aim of this study was to introduce methods for benchmarking the suitability of new EEG technologies for that context. Subjects performed an auditory oddball task using three different EEG systems (Biosemi wet-BSM, Cognionics Wet-Cwet, Conionics Dry-Cdry). Nine subjects performed the oddball task while seated and walking on a treadmill. We calculated EEG epoch rejection rate, pre-stimulus noise (PSN), signal-to-noise ratio (SNR) and EEG amplitude variance across the P300 event window (CVERP) from a subset of 12 channels common to all systems. We also calculated test-retest reliability and the subject's level of comfort while using each system. Our results showed that using the traditional 75 µV rejection threshold BSM and Cwet epoch rejection rates are ~25% and ~47% in the seated and walking conditions respectively. However, this threshold rejects ~63% of epochs for Cdry in the seated condition and excludes 100% of epochs for the majority of subjects during walking. BSM showed predominantly no statistical differences between seated and walking condition for all metrics, whereas Cwet showed increases in PSN and CVERP, as well as reduced SNR in the walking condition. Data quality from Cdry in seated conditions were predominantly inferior in comparison to the wet systems. Test-retest reliability was mostly moderate/good for these variables, especially in seated conditions. In addition, subjects felt less discomfort and were motivated for longer recording periods while using wet EEG systems in comparison to the dry system. The proposed method was successful in identifying differences across systems that are mostly caused by motion-related artifacts and usability issues. We conclude that the extraction of the selected metrics from an auditory oddball paradigm may be used as a benchmark method for testing the performance of different EEG systems in mobile conditions. Moreover dry EEG systems may need substantial improvements to meet the quality standards of wet electrodes.