A practical guide to EEG hyperscanning in joint action research: from motivation to implementation.

Developments in cognitive neuroscience have led to the emergence of hyperscanning, the simultaneous measurement of brain activity from multiple people. Hyperscanning is useful for investigating social cognition, including joint action, because of its ability to capture neural processes that occur within and between people as they coordinate actions toward a shared goal. Here, we provide a practical guide for researchers considering using hyperscanning to study joint action and seeking to avoid frequently raised concerns from hyperscanning skeptics. We focus specifically on Electroencephalography (EEG) hyperscanning, which is widely available and optimally suited for capturing fine-grained temporal dynamics of action coordination. Our guidelines cover questions that are likely to arise when planning a hyperscanning project, ranging from whether hyperscanning is appropriate for answering one's research questions to considerations for study design, dependent variable selection, data analysis and visualization. By following clear guidelines that facilitate careful consideration of the theoretical implications of research design choices and other methodological decisions, joint action researchers can mitigate interpretability issues and maximize the benefits of hyperscanning paradigms.

At-home sleep monitoring using generic ear-EEG.

Introduction: A device comprising two generic earpieces with embedded dry electrodes for ear-centered electroencephalography (ear-EEG) was developed. The objective was to provide ear-EEG based sleep monitoring to a wide range of the population without tailoring the device to the individual. Methods: To validate the device ten healthy subjects were recruited for a 12-night sleep study. The study was divided into two parts; part A comprised two nights with both ear-EEG and polysomnography (PSG), and part B comprised 10 nights using only ear-EEG. In addition to the electrophysiological measurements, subjects filled out a questionnaire after each night of sleep. Results: The subjects reported that the ear-EEG system was easy to use, and that the comfort was better in part B. The performance of the system was validated by comparing automatic sleep scoring based on ear-EEG with PSG-based sleep scoring performed by a professional trained sleep scorer. Cohen's kappa was used to assess the agreement between the manual and automatic sleep scorings, and the study showed an average kappa value of 0.71. The majority of the 20 recordings from part A yielded a kappa value above 0.7. The study was compared to a companioned study conducted with individualized earpieces. To compare the sleep across the two studies and two parts, 7 different sleeps metrics were calculated based on the automatic sleep scorings. The ear-EEG nights were validated through linear mixed model analysis in which the effects of equipment (individualized vs. generic earpieces), part (PSG and ear-EEG vs. only ear-EEG) and subject were investigated. We found that the subject effect was significant for all computed sleep metrics. Furthermore, the equipment did not show any statistical significant effect on any of the sleep metrics. Discussion: These results corroborate that generic ear-EEG is a promising alternative to the gold standard PSG for sleep stage monitoring. This will allow sleep stage monitoring to be performed in a less obtrusive way and over longer periods of time, thereby enabling diagnosis and treatment of diseases with associated sleep disorders.

Characterization of Dry-Contact EEG Electrodes and an Empirical Comparison of Ag/AgCl and IrO2 Electrodes.

Dry-contact electrodes are increasingly being used for EEG recordings in both research studies and consumer products. They are more user-friendly and better suited for long-term recordings. However, dry-contact electrodes also bring challenges with respect to the stability and impedance of the electrode-skin interface. We propose a methodology to characterize and compare dry-contact electrodes. The characterization is based on measuring the electrode-skin impedance spectrum, fit a parametric model of the electrode-skin interface to the measured spectrum, and calculate the resulting thermal noise spectrum. Thereby it is possible to relate the noise of an EEG recording to the theoretical noise contribution from the electrode-skin interface. To demonstrate the methodology, we performed an empirical study comparing two types of dry-contact electrodes in an ear-EEG setup. The electrodes were IrO2, previously used for ear-EEG, and a new design based on Ag/AgCl. Here, we related the noise floor of an auditory steady-state response (ASSR) to the thermal noise spectrum of the electrode-skin interface. The study showed similar impedance and EEG recording quality for the two electrode types, and the thermal noise of the electrode-skin interface was below the noise floor of the EEG recordings for both electrode types. Dry-contact EEG is an enabling technology for long-term brain monitoring of patients. This may be relevant for example for monitoring of neurodegenerative diseases, stroke patients, patients with traumatic brain injuries, and psychiatric patients.

Decoding of Hand Gestures from Electrocorticography with LSTM Based Deep Neural Network.

Hand gesture decoding is a key component of controlling prosthesis in the area of Brain Computer Interface (BCI). This study is concerned with classification of hand gestures, based on Electrocorticography (ECoG) recordings. Recent studies have utilized the temporal information in ECoG signals for robust hand gesture decoding. In our preliminary analysis on ECoG recordings of hand gestures, we observed different power variations in six frequency bands ranging from 4 to 200 Hz. Therefore, the current trend of including temporal information in the classifier was extended to provide equal importance to power variations in each of these frequency bands. Statistical and Principal Component Analysis (PCA) based feature reduction was implemented for each frequency band separately, and classification was performed with a Long Short-Term Memory (LSTM) based neural network to utilize both temporal and spatial information of each frequency band. The proposed architecture along with each feature reduction method was tested on ECoG recordings of five finger flexions performed by seven subjects from the publicly available 'fingerflex' dataset. An average classification accuracy of 82.4% was achieved with the statistical based channel selection method which is an improvement compared to state-of-the-art methods.

Accurate whole-night sleep monitoring with dry-contact ear-EEG.

Sleep is a key phenomenon to both understanding, diagnosing and treatment of many illnesses, as well as for studying health and well being in general. Today, the only widely accepted method for clinically monitoring sleep is the polysomnography (PSG), which is, however, both expensive to perform and influences the sleep. This has led to investigations into light weight electroencephalography (EEG) alternatives. However, there has been a substantial performance gap between proposed alternatives and PSG. Here we show results from an extensive study of 80 full night recordings of healthy participants wearing both PSG equipment and ear-EEG. We obtain automatic sleep scoring with an accuracy close to that achieved by manual scoring of scalp EEG (the current gold standard), using only ear-EEG as input, attaining an average Cohen's kappa of 0.73. In addition, this high performance is present for all 20 subjects. Finally, 19/20 subjects found that the ear-EEG had little to no negative effect on their sleep, and subjects were generally able to apply the equipment without supervision. This finding marks a turning point on the road to clinical long term sleep monitoring: the question should no longer be whether ear-EEG could ever be used for clinical home sleep monitoring, but rather when it will be.

Ear-EEG Forward Models: Improved Head-Models for Ear-EEG.

Computational models for mapping electrical sources in the brain to potentials on the scalp have been widely explored. However, current models do not describe the external ear anatomy well, and is therefore not suitable for ear-EEG recordings. Here we present an extension to existing computational models, by incorporating an improved description of the external ear anatomy based on 3D scanned impressions of the ears. The result is a method to compute an ear-EEG forward model, which enables mapping of sources in the brain to potentials in the ear. To validate the method, individualized ear-EEG forward models were computed for four subjects, and ear-EEG and scalp EEG were recorded concurrently from the subjects in a study comprising both auditory and visual stimuli. The EEG recordings were analyzed with independent component analysis (ICA) and using the individualized ear-EEG forward models, single dipole fitting was performed for each independent component (IC). A subset of ICs were selected, based on how well they were modeled by a single dipole in the brain volume. The correlation between the topographic IC map and the topographic map predicted by the forward model, was computed for each IC. Generally, the correlation was high in the ear closest to the dipole location, showing that the ear-EEG forward models provided a good model to predict ear potentials. In addition, we demonstrated that the developed forward models can be used to explore the sensitivity to brain sources for different ear-EEG electrode configurations. We consider the proposed method to be an important step forward in the characterization and utilization of ear-EEG.

Dry-Contact Electrode Ear-EEG.

OBJECTIVE: Ear-EEG is a recording method in which EEG signals are acquired from electrodes placed on an earpiece inserted into the ear. Thereby, ear-EEG provides a noninvasive and discreet way of recording EEG, and has the potential to be used for long-term brain monitoring in real-life environments. Whereas previously reported ear-EEG recordings have been performed with wet electrodes, the objective of this study was to develop and evaluate dry-contact electrode ear-EEG. METHODS: To achieve a well-functioning dry-contact interface, a new ear-EEG platform was developed. The platform comprised actively shielded and nanostructured electrodes embedded in an individualized soft-earpiece. The platform was evaluated in a study of 12 subjects and four EEG paradigms: auditory steady-state response, steady-state visual evoked potential, mismatch negativity, and alpha-band modulation. RESULTS: Recordings from the prototyped dry-contact ear-EEG platform were compared to conventional scalp EEG recordings. When all electrodes were referenced to a common scalp electrode (Cz), the performance was on par with scalp EEG measured close to the ear. With both the measuring electrode and the reference electrode located within the ear, statistically significant (p < 0.05) responses were measured for all paradigms, although for mismatch negativity, it was necessary to use a reference located in the opposite ear, to obtain a statistically significant response. CONCLUSION: The study demonstrated that dry-contact electrode ear-EEG is a feasible technology for EEG recording. SIGNIFICANCE: The prototyped dry-contact ear-EEG platform represents an important technological advancement of the method in terms of user-friendliness, because it eliminates the need for gel in the electrode-skin interface.

Discrimination of Sleep Spindles in Ear-EEG.

Sleep spindles are brief oscillatory events observed in EEG measurements during sleep, related to both sleep staging and basic neuroscience. The objective of this study was to investigate to which extent sleep spindles are observable from ear-EEG. The analysis was based on single-night recordings from 12 subjects, wearing both a polysomnography setup and two light-weight mobile EEG devices (ear-EEG). By introducing a sleep spindle index capable of discriminating between epochs with distinct spindles and distinctly spindle-free epochs, we describe to which extent the most clear cut sleep spindles (as labeled using scalp EEG) can be detected using ear-EEG. We find that ear-EEG can be used to detect sleep spindles, at a performance level similar to scalp derivations. We speculate that part of the observed discrepancy between ear-EEG and the gold standard (scalp EEG) could be caused by the visibility of different spindles in the ear-EEG.

Real-Life Dry-Contact Ear-EEG.

Our brain state is affected by and adapted to our surroundings. Therefore, to study natural states of the brain, it is desirable to measure brain responses in natural environments outside the lab. Among functional brain scanning methods, electroencephalography (EEG) is the most promising method for non-invasive brain monitoring in real-life environments. To enable long-term recordings in real-life, EEG devices must be wearable, user-friendly, and discreet. Ear-EEG is a method where EEG signals are recorded from electrodes placed on an earpiece inserted into the ear. The compact and discreet nature of an ear-EEG device makes it suitable for long-term real-life recordings. In this study, 6 subjects were recorded with conventional scalp EEG and ear-EEG. All recordings were performed with the same instrumentation and paradigms in both a lab setting and a real-life setting. The ear-EEG recordings were performed with a previously developed drycontact ear-EEG platform. Signals from the scalp electrodes and ear-electrodes were recorded by the same biosignal recorder, enabling re-referencing in the post-processing and analysis. The study comprised four paradigms: auditory steady-state response (ASSR), steady-state visual evoked potential (SSVEP), auditory onset response, and alpha band modulation. When the data were analyzed with a scalp reference (Cz), all the investigated responses were statistically significant in recordings from both settings. Statistically significant ASSR and SSVEP were measured in the lab by ear-electrodes referenced to an electrode within the same ear. In real-life, only the ASSR was statistically significant for a reference within the same ear. The results demonstrates that electrical brain activity can be recorded from dry-contact electrode ear-EEG in real-life.

High-density ear-EEG.

Ear-EEG enables recording of EEG in real-life environments in an unprecedented discreet and minimal obtrusive way. As ear-EEG are recorded from electrodes placed in or around the ear, the spatial coverage of the potential field on the scalp is inherently limited. Despite the limited spatial coverage, the potential field in-the-ear can still be measured in multiple points and thereby provide spatial information. We present a method to perform and visualize high-density ear-EEG recordings, and illustrate the method through recordings of auditory and visually evoked steady-state responses, for a single subject. The auditory and visually evoked responses showed distinctive differences in the response field in the ear, reflecting the very different locations of the underlying cortical sources. In conclusion, high-density ear-EEG can be used to investigate how different cortical sources maps to the ear, and provides a way to select optimal electrode positions for given brain phenomena.

Physiological artifacts in scalp EEG and ear-EEG.

BACKGROUND: A problem inherent to recording EEG is the interference arising from noise and artifacts. While in a laboratory environment, artifacts and interference can, to a large extent, be avoided or controlled, in real-life scenarios this is a challenge. Ear-EEG is a concept where EEG is acquired from electrodes in the ear. METHODS: We present a characterization of physiological artifacts generated in a controlled environment for nine subjects. The influence of the artifacts was quantified in terms of the signal-to-noise ratio (SNR) deterioration of the auditory steady-state response. Alpha band modulation was also studied in an open/closed eyes paradigm. RESULTS: Artifacts related to jaw muscle contractions were present all over the scalp and in the ear, with the highest SNR deteriorations in the gamma band. The SNR deterioration for jaw artifacts were in general higher in the ear compared to the scalp. Whereas eye-blinking did not influence the SNR in the ear, it was significant for all groups of scalps electrodes in the delta and theta bands. Eye movements resulted in statistical significant SNR deterioration in both frontal, temporal and ear electrodes. Recordings of alpha band modulation showed increased power and coherence of the EEG for ear and scalp electrodes in the closed-eyes periods. CONCLUSIONS: Ear-EEG is a method developed for unobtrusive and discreet recording over long periods of time and in real-life environments. This study investigated the influence of the most important types of physiological artifacts, and demonstrated that spontaneous activity, in terms of alpha band oscillations, could be recorded from the ear-EEG platform. In its present form ear-EEG was more prone to jaw related artifacts and less prone to eye-blinking artifacts compared to state-of-the-art scalp based systems.

Reference configurations for ear-EEG steady-state responses.

Ear-EEG is a non-invasive EEG recording method, where EEG is recorded from electrodes placed in the ear. Ear-EEG could be implemented into hearing aids, and provide neurofeedback for e.g. objective hearing assessment through measurements of the auditory steady-state response. In cases where the objective is to measure a specific feature of an event-related potential, there will be a subject specific optimal reference configuration. This work presents a method for optimizing the reference configuration for steady-state type potentials. For given electrode positions, the method maximizes the signal-to-noise (SNR) ratio of the first harmonic of the steady-state response. This is obtained by estimating a set of weights applied to the electrode signals. The method was validated on a dataset recorded from 12 subjects. The weights were estimated from one part of the dataset, and the validation was performed on another part of the dataset. For all subjects the proposed method demonstrated a robust SNR estimate, yielding on par or better SNR compared to other well-known methods.

EEG Recorded from the Ear: Characterizing the Ear-EEG Method.

Highlights Auditory middle and late latency responses can be recorded reliably from ear-EEG.For sources close to the ear, ear-EEG has the same signal-to-noise-ratio as scalp.Ear-EEG is an excellent match for power spectrum-based analysis. A method for measuring electroencephalograms (EEG) from the outer ear, so-called ear-EEG, has recently been proposed. The method could potentially enable robust recording of EEG in natural environments. The objective of this study was to substantiate the ear-EEG method by using a larger population of subjects and several paradigms. For rigor, we considered simultaneous scalp and ear-EEG recordings with common reference. More precisely, 32 conventional scalp electrodes and 12 ear electrodes allowed a thorough comparison between conventional and ear electrodes, testing several different placements of references. The paradigms probed auditory onset response, mismatch negativity, auditory steady-state response and alpha power attenuation. By comparing event related potential (ERP) waveforms from the mismatch response paradigm, the signal measured from the ear electrodes was found to reflect the same cortical activity as that from nearby scalp electrodes. It was also found that referencing the ear-EEG electrodes to another within-ear electrode affects the time-domain recorded waveform (relative to scalp recordings), but not the timing of individual components. It was furthermore found that auditory steady-state responses and alpha-band modulation were measured reliably with the ear-EEG modality. Finally, our findings showed that the auditory mismatch response was difficult to monitor with the ear-EEG. We conclude that ear-EEG yields similar performance as conventional EEG for spectrogram-based analysis, similar timing of ERP components, and equal signal strength for sources close to the ear. Ear-EEG can reliably measure activity from regions of the cortex which are located close to the ears, especially in paradigms employing frequency-domain analyses.

Dynamic navicular motion measured using a stretch sensor is different between walking and running, and between over-ground and treadmill conditions.

BACKGROUND: Non-invasive evaluation of in-shoe foot motion has traditionally been difficult. Recently a novel 'stretch-sensor' was proposed as an easy and reliable method to measure dynamic foot (navicular) motion. Further validation of this method is needed to determine how different gait analysis protocols affect dynamic navicular motion. METHODS: Potential differences in magnitude and peak velocity of navicular motion using the 'stretch sensor' between (i) barefoot and shod conditions; (ii) overground and treadmill gait; and/or (iii) running and walking were evaluated in 26 healthy participants. Comparisons were made using paired t-tests. RESULTS: Magnitude and velocity of navicular motion was not different between barefoot and shod walking on the treadmill. Compared to walking, velocity of navicular motion during running was 59% and 210% higher over-ground (p < 0.0001) and on a treadmill (p < 0.0001) respectively, and magnitude of navicular motion was 23% higher during over-ground running compared to over-ground walking (p = 0.02). Compared to over-ground, magnitude of navicular motion on a treadmill was 21% and 16% greater during walking (p = 0.0004) and running (p = 0003) respectively. Additionally, maximal velocity of navicular motion during treadmill walking was 48% less than walking over-ground (p < 0.0001). CONCLUSION: The presence of footwear has minimal impact on navicular motion during walking. Differences in navicular motion between walking and running, and treadmill and over-ground gait highlight the importance of task specificity during gait analysis. Task specificity should be considered during design of future research trials and in clinical practice when measuring navicular motion.

Study of impedance spectra for dry and wet EarEEG electrodes.

EarEEG is a novel recordings concept where electrodes are embedded on the surface of an earpiece customized to the individual anatomical shape of the users ear. A key parameter for recording EEG signals of good quality is a stable and low impedance electrode-body interface. This study characterizes the impedance for dry and wet EarEEG electrodes in a study of 10 subjects. A custom made and automated setup was used to characterize the impedance spectrum from 0.1 Hz-2 kHz. The study of dry electrodes showed a mean (standard deviation) low frequency impedance of the canal electrodes of 1.2 MΩ (1.4 MΩ) and the high frequency impedance was 230 kΩ (220 kΩ). For wet electrodes the low frequency impedance was 34 kΩ (37 kΩ) and the high frequency impedance was 5.1 kΩ (4.4 kΩ). The high standard deviation of the impedance for dry electrodes imposes very high requirements for the input impedance of the amplifier in order to achieve an acceptable common-mode rejection. The wet electrode impedance was in line with what is typical for a wet electrode interface.

Reliability and concurrent validity of a novel method allowing for in-shoe measurement of navicular drop.

BACKGROUND: Increased navicular drop is associated with increased risk of lower extremity overuse injuries and foot orthoses are often prescribed to reduce navicular drop. For laboratory studies, transparent shoes may be used to monitor the effect of orthoses but no clinically feasible methods exist. We have developed a stretch-sensor that allows for in-shoe measurement of navicular drop but the reliability and validity is unknown. The purpose of this study was to investigate: 1) the reliability of the stretch-sensor for measuring navicular drop, and 2) the concurrent validity of the stretch-sensor compared to the static navicular drop test. METHODS: Intra- and inter-rater reliability was tested on 27 participants walking on a treadmill on two separate days. The stretch-sensor was positioned 20 mm posterior to the tip of the medial malleolus and 20 mm posterior to the navicular tuberosity. The participants walked six minutes on the treadmill before navicular drop was measured. Reliability was quantified by the Intraclass Correlation Coefficient (ICC 2.1) and agreement was quantified by Limits of Agreement (LOA). To assess concurrent validity, static navicular drop was measured with the stretch-sensor and compared with static navicular drop measured with a ruler on 27 new participants. Linear regression was used to measure concurrent validity. RESULTS: The reliability of the stretch-sensor was acceptable for barefoot measurement (intra- and inter-rater ICC: 0.76-0.84) but lower for in-shoe measurement (ICC: 0.65). There was a significant association between static navicular drop measured with the stretch-sensor compared with a ruler (r = 0.745, p < 0.001). CONCLUSION: This study suggests that the stretch-sensor has acceptable reliability for dynamic barefoot measurement of navicular drop. Furthermore, the stretch-sensor shows concurrent validity compared with the static navicular drop test as performed by Brody. This new simple method may hold promise for both clinical assessment and research but more work is needed before the method can be recommended.

A method for quantitative assessment of artifacts in EEG, and an empirical study of artifacts.

Wearable EEG systems for continuous brain monitoring is an emergent technology that involves significant technical challenges. Some of these are related to the fact that these systems operate in conditions that are far less controllable with respect to interference and artifacts than is the case for conventional systems. Quantitative assessment of artifacts provides a mean for optimization with respect to electrode technology, electrode location, electronic instrumentation and system design. To this end, we propose an artifact assessment method and evaluate it over an empirical study of 3 subjects and 5 different types of artifacts. The study showed consistent results across subjects and artifacts.

A novel method for measuring in-shoe navicular drop during gait.

Analysis of foot movement is essential in the treatment and prevention of foot-related disorders. Measuring the in-shoe foot movement during everyday activities, such as sports, has the potential to become an important diagnostic tool in clinical practice. The current paper describes the development of a thin, flexible and robust capacitive strain sensor for the in-shoe measurement of the navicular drop. The navicular drop is a well-recognized measure of foot movement. The position of the strain sensor on the foot was analyzed to determine the optimal points of attachment. The sensor was evaluated against a state-of-the-art video-based system that tracks reflective markers on the bare foot. Preliminary experimental results show that the developed strain sensor is able to measure navicular drop on the bare foot with an accuracy on par with the video-based system and with a high reproducibility. Temporal comparison of video-based, barefoot and in-shoe measurements indicate that the developed sensor measures the navicular drop accurately in shoes and can be used without any discomfort for the user.