Effect of Stimulus Bandwidth on the Auditory Steady-State Response in Scalp- and Ear-EEG

OBJECTIVES

The auditory steady-state response (ASSR) enables hearing threshold estimation based on electroencephalography (EEG) recordings. The choice of stimulus type has an impact on both the detectability and the frequency specificity of the ASSR. Amplitude modulated pure tones provide the most frequency-specific ASSR, but responses to pure tones are weak. The ASSR can be enhanced by increasing the bandwidth of the stimulus, but this comes at the cost of a decrease in the frequency specificity of the measured response. The objective of the present study is to investigate the relationship between stimulus bandwidth and ASSR amplitude.

DESIGN

The amplitude of ASSR was measured for five types of stimuli: 1 kHz pure tone and band-pass noise with 1/3, 1/2, 1, and 2 octave bandwidths centered at 1 kHz. All stimuli were amplitude modulated with a 40 Hz sinusoid. Responses to all stimulus types were measured at 30, 40, and 50 dB SL. ASSRs were measured concurrently using both conventional scalp-EEG and ear-EEG.

RESULTS

Stimulus bandwidth and sound intensity were both found to have a significant effect on the ASSR amplitude for scalp- and ear-EEG recordings. In scalp-EEG ASSRs to all bandwidth stimuli were found to be significantly larger than ASSRs to pure tone at low sound intensity. At higher sound intensities, however, significantly larger responses were only obtained for 1- and 2-octave bandwidth stimuli. In ear-EEG, only the ASSR to 2 octave bandwidth stimulus was significantly larger than the ASSR to amplitude modulated pure tones.

CONCLUSIONS

At low presentation levels, even small increases in stimulus bandwidth (1/3 and 1/2 octave) improve the detectability of ASSR in scalp-EEG with little or no impact on the frequency specificity. In comparison, a larger increase in stimulus bandwidth was needed to improve the ASSR detectability in the ear-EEG recordings.

Towards ASSR-based hearing assessment using natural sounds

Objective. The auditory steady-state response (ASSR) allows estimation of hearing thresholds. The ASSR can be estimated from electroencephalography (EEG) recordings from electrodes positioned on both the scalp and within the ear (ear-EEG). Ear-EEG can potentially be integrated into hearing aids, which would enable automatic fitting of the hearing device in daily life. The conventional stimuli for ASSR-based hearing assessment, such as pure tones and chirps, are monotonous and tiresome, making them inconvenient for repeated use in everyday situations. In this study we investigate the use of natural speech sounds for ASSR estimation.Approach.EEG was recorded from 22 normal hearing subjects from both scalp and ear electrodes. Subjects were stimulated monaurally with 180 min of speech stimulus modified by applying a 40 Hz amplitude modulation (AM) to an octave frequency sub-band centered at 1 kHz. Each 50 ms sub-interval in the AM sub-band was scaled to match one of 10 pre-defined levels (0-45 dB sensation level, 5 dB steps). The apparent latency for the ASSR was estimated as the maximum average cross-correlation between the envelope of the AM sub-band and the recorded EEG and was used to align the EEG signal with the audio signal. The EEG was then split up into sub-epochs of 50 ms length and sorted according to the stimulation level. ASSR was estimated for each level for both scalp- and ear-EEG.Main results. Significant ASSRs with increasing amplitude as a function of presentation level were recorded from both scalp and ear electrode configurations.Significance. Utilizing natural sounds in ASSR estimation offers the potential for electrophysiological hearing assessment that are more comfortable and less fatiguing compared to existing ASSR methods. Combined with ear-EEG, this approach may allow convenient hearing threshold estimation in everyday life, utilizing ambient sounds. Additionally, it may facilitate both initial fitting and subsequent adjustments of hearing aids outside of clinical settings.

Subclinical epileptiform discharges in Alzheimer's disease are associated with increased hippocampal blood flow

BACKGROUND

In epilepsy, the ictal phase leads to cerebral hyperperfusion while hypoperfusion is present in the interictal phases. Patients with Alzheimer's disease (AD) have an increased prevalence of epileptiform discharges and a study using intracranial electrodes have shown that these are very frequent in the hippocampus. However, it is not known whether there is an association between hippocampal hyperexcitability and regional cerebral blood flow (rCBF). The objective of the study was to investigate the association between rCBF in hippocampus and epileptiform discharges as measured with ear-EEG in patients with Alzheimer's disease. Our hypothesis was that increased spike frequency may be associated with increased rCBF in hippocampus.

METHODS

A total of 24 patients with AD, and 15 HC were included in the analysis. Using linear regression, we investigated the association between rCBF as measured with arterial spin-labelling MRI (ASL-MRI) in the hippocampus and the number of spikes/sharp waves per 24 h as assessed by ear-EEG.

RESULTS

No significant difference in hippocampal rCBF was found between AD and HC (p-value = 0.367). A significant linear association between spike frequency and normalized rCBF in the hippocampus was found for patients with AD (estimate: 0.109, t-value = 4.03, p-value < 0.001). Changes in areas that typically show group differences (temporal-parietal cortex) were found in patients with AD, compared to HC.

CONCLUSIONS

Increased spike frequency was accompanied by a hemodynamic response of increased blood flow in the hippocampus in patients with AD. This phenomenon has also been shown in patients with epilepsy and supports the hypothesis of hyperexcitability in patients with AD. The lack of a significant difference in hippocampal rCBF may be due to an increased frequency of epileptiform discharges in patients with AD.

TRIAL REGISTRATION

The study is registered at clinicaltrials.gov (NCT04436341).

Subclinical Epileptiform Activity in Dementia with Lewy Bodies

BACKGROUND

Patients with dementia with Lewy bodies (DLB) have a higher probability of seizures than in normal aging and in other types of neurodegenerative disorders. Depositions of α-synuclein, a pathological hallmark of DLB, can induce network excitability, which can escalate into seizure activity. Indicator of seizures are epileptiform discharges as observed using electroencephalography (EEG). However, no studies have so far investigated the occurrence of interictal epileptiform discharges (IED) in patients with DLB.

OBJECTIVES

To investigate if IED as measured with ear-EEG occurs with a higher frequency in patients with DLB compared to healthy controls (HC).

METHODS

In this longitudinal observational exploratory study, 10 patients with DLB and 15 HC were included in the analysis. Patients with DLB underwent up to three ear-EEG recordings, each lasting up to 2 days, over a period of 6 months.

RESULTS

At baseline, IED were detected in 80% of patients with DLB and in 46.7% of HC. The spike frequency (spikes or sharp waves/24 hours) was significantly higher in patients with DLB as compared to HC with a risk ratio of 2.52 (CI, 1.42-4.61; P-value = 0.001). Most IED occurred at night.

CONCLUSIONS

Long-term outpatient ear-EEG monitoring detects IED in most patients with DLB with an increased spike frequency compared to HC. This study extends the spectrum of neurodegenerative disorders in which epileptiform discharges occurs at an elevated frequency. It is possible that epileptiform discharges are, therefore, a consequence of neurodegeneration. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Long-term ear-EEG monitoring of sleep - A case study during shift work

The interest in sleep as a potential clinical biomarker is growing, but the standard method of sleep assessment, polysomnography, is expensive, time consuming, and requires a lot of expert assistance for both set-up and interpretation. To make sleep analysis more available both in research and in the clinic, there is a need for a reliable wearable device for sleep staging. In this case study, we test ear-electroencephalography. A wearable, where electrodes are placed in the outer ear, as a platform for longitudinal at-home recording of sleep. We explore the usability of the ear-electroencephalography in a shift work case with alternating sleep conditions. We find the ear-electroencephalography platform to be reliable both in terms of showing substantial agreement to polysomnography after long-time use (with an overall agreement, using Cohen's kappa, of 0.72) and by being unobtrusive enough to wear during night shift conditions. We find that fractions of non-rapid eye movement sleep and transition probability between sleep stages show great potential as sleep metrics when exploring quantitative differences in sleep architecture between shifting sleep conditions. This study shows that the ear-electroencephalography platform holds great potential as a reliable wearable for quantifying sleep "in the wild", pushing this technology further towards clinical adaptation.

Detection of subclinical epileptiform discharges in Alzheimer's disease using long-term outpatient EEG monitoring

BACKGROUND

In patients with Alzheimer's disease (AD) without clinical seizures, up to half have epileptiform discharges on long-term in-patient electroencephalography (EEG) recordings. Long-term in-patient monitoring is obtrusive, and expensive as compared to outpatient monitoring. No studies have so far investigated if long-term outpatient EEG monitoring is able to identify epileptiform discharges in AD. Our aim is to investigate if epileptiform discharges as measured with ear-EEG are more common in patients with AD compared to healthy elderly controls (HC).

METHODS

In this longitudinal observational study, 24 patients with mild to moderate AD and 15 age-matched HC were included in the analysis. Patients with AD underwent up to three ear-EEG recordings, each lasting up to two days, within 6 months.

RESULTS

The first recording was defined as the baseline recording. At baseline, epileptiform discharges were detected in 75.0% of patients with AD and in 46.7% of HC (p-value = 0.073). The spike frequency (spikes or sharp waves/24 h) was significantly higher in patients with AD as compared to HC with a risk ratio of 2.90 (CI: 1.77-5.01, p < 0.001). Most patients with AD (91.7%) showed epileptiform discharges when combining all ear-EEG recordings.

CONCLUSIONS

Long-term ear-EEG monitoring detects epileptiform discharges in most patients with AD with a three-fold increased spike frequency compared to HC, which most likely originates from the temporal lobes. Since most patients showed epileptiform discharges with multiple recordings, elevated spike frequency should be considered a marker of hyperexcitability in AD.

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.

Long-Term EEG Monitoring in Patients with Alzheimer's Disease Using Ear-EEG: A Feasibility Study

BACKGROUND

Previous studies have reported that epileptiform activity may be detectible in nearly half of patients with Alzheimer's disease (AD) on long-term electroencephalographic (EEG) recordings. However, such recordings can be uncomfortable, expensive, and difficult. Ear-EEG has shown promising results for long-term EEG monitoring, but it has not been used in patients with AD.

OBJECTIVE

To investigate if ear-EEG is a feasible method for long-term EEG monitoring in patients with AD.

METHODS

In this longitudinal, single-group feasibility study, ten patients with mild to moderate AD were recruited. A total of three ear-EEG recordings of up to 48 hours three months apart for six months were planned.

RESULTS

All patients managed to wear the ear-EEG for at least 24 hours and at least one full night. A total of 19 ear-EEG recordings were performed (self-reported recording, mean: 37.15 hours (SD: 8.96 hours)). After automatic pre-processing, a mean of 27.37 hours (SD: 7.19 hours) of data with acceptable quality in at least one electrode in each ear was found. Seven out of ten participants experienced mild adverse events. Six of the patients did not complete the study with three patients not wanting to wear the ear-EEG anymore due to adverse events.

CONCLUSION

It is feasible and safe to use ear-EEG for long-term EEG monitoring in patients with AD. Minor adjustments to the equipment may improve the comfort for the participants.

Repeated automatic sleep scoring based on ear-EEG is a valuable alternative to manually scored polysomnography

While polysomnography (PSG) is the gold standard to quantify sleep, modern technology allows for new alternatives. PSG is obtrusive, affects the sleep it is set out to measure and requires technical assistance for mounting. A number of less obtrusive solutions based on alternative methods have been introduced, but few have been clinically validated. Here we validate one of these solutions, the ear-EEG method, against concurrently recorded PSG in twenty healthy subjects each measured for four nights. Two trained technicians scored the 80 nights of PSG independently, while an automatic algorithm scored the ear-EEG. The sleep stages and eight sleep metrics (Total Sleep Time (TST), Sleep Onset Latency, Sleep Efficiency, Wake After Sleep Onset, REM latency, REM fraction of TST, N2 fraction of TST, and N3 fraction of TST) were used in the further analysis. We found the sleep metrics: Total Sleep Time, Sleep Onset Latency, Sleep Efficiency, Wake After Sleep Onset were estimated with high accuracy and precision between automatic sleep scoring and manual sleep scoring. However, the REM latency and REM fraction of sleep showed high accuracy but low precision. Further, the automatic sleep scoring systematically overestimated the N2 fraction of sleep and slightly underestimated the N3 fraction of sleep. We demonstrate that sleep metrics estimated from automatic sleep scoring based on repeated ear-EEG in some cases are more reliably estimated with repeated nights of automatically scored ear-EEG than with a single night of manually scored PSG. Thus, given the obtrusiveness and cost of PSG, ear-EEG seems to be a useful alternative for sleep staging for the single night recording and an advantageous choice for several nights of sleep monitoring.

Investigation of the Effect of Spatial Filtering for Detecting Auditory Steady-State Responses Recorded from Ear-EEG

Auditory steady-state responses (ASSRs) enable hearing threshold estimation based on electrophysiological measurements and are widely used in clinical practice. Traditionally, ASSRs are recorded from a few electroencephalography (EEG) electrodes placed on the scalp. Ear-EEG is a method in which the EEG is recorded from electrodes placed within or around the ear and is thus more suitable for use in everyday life. Ear-EEG is typically recorded from multiple electrodes in order to enhance redundancy and robustness, but a pair of electrodes (so-called "best pair") is usually chosen for the further analysis. Spatial filtering uses an optimized weighted combination of the electrodes, and is thus in general a better method for analysis of multichannel EEG. In this study we propose a new spatial filtering method based on solving a constrained optimization problem. Empirical evaluation based on ear-EEG recorded from nine subjects shows that the proposed spatial filtering method provides a significant increase in ASSR SNR as compared to the conventional "best pair" method. Clinical Relevance - ASSR can be estimated from ear-EEG recordings. Integrating ear-EEG into hearing aids would allow hearing aids to characterize hearing loss and thereby adjust the audio processing accordingly.

Self-applied ear-EEG for sleep monitoring at home

High quality sleep monitoring is done using EEG electrodes placed on the skin. This has traditionally required assistance by an expert when the equipment needed to mounted. However, this creates a limitation in how cheap and easy it can be to record sleep in the subject's own home. Here we present a data set of 120 home recordings of sleep, in which subjects use self-applied ear-EEG monitoring equipment. We compare this data set to a previously recorded data set with both ear-EEG and polysomnography, which was applied by an expert. Clinical relevance - On all tested metrics, self applied sleep recordings behaved the same as expert applied. This indicates that ear-EEG can reliably be used as a home sleep monitor, even when subjects apply the equipment themselves.

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.

Sleep monitoring using ear-centered setups: Investigating the influence from electrode configurations

Modern sleep monitoring development is shifting towards the use of unobtrusive sensors combined with algorithms for automatic sleep scoring. Many different combinations of wet and dry electrodes, ear-centered, forehead-mounted or headband-inspired designs have been proposed, alongside an ever growing variety of machine learning algorithms for automatic sleep scoring.

OBJECTIVE

Among candidate positions, those in the facial area and around the ears have the benefit of being relatively hairless, and in our view deserve extra attention. In this paper, we seek to determine the limits to sleep monitoring quality within this spatial constraint.

METHODS

We compare 13 different, realistic sensor setups derived from the same data set and analysed with the same pipeline.

RESULTS

All setups which include both a lateral and an EOG derivation show similar, state-of-the-art performance, with average Cohen's kappa values of at least 0.80.

CONCLUSION

If large electrode distances are used, positioning is not critical for achieving large sleep-related signal-to-noise-ratio, and hence accurate sleep scoring.

SIGNIFICANCE

We argue that with the current competitive performance of automated staging approaches, there is a need for establishing an improved benchmark beyond current single human rater scoring.

Chirp-evoked auditory steady-state response: The effect of repetition rate

OBJECTIVE

The auditory steady-state response (ASSR) is commonly used in clinical pediatric audiology in order to provide an electrophysiological estimate of hearing threshold, and has the potential to be used in unsupervised mobile EEG applications. Enhancement of the ASSR amplitude through optimization of the stimulation and recording methods shortens the required testing time or reduce the offset between the electrophysiological and behavioral thresholds. Here, we investigate the spatial distribution of the ASSR to broadband chirp stimuli across a wide range of repetition rates on the scalp and in the ears. Moreover, the ASSR amplitude is compared across repetition rates for commonly used electrode configurations.

METHODS

ASSR to chirp stimuli with repetition rates from 6-198 Hz was recorded using scalp EEG and high-density ear-EEG.

RESULTS

The distributions of the ASSR amplitude and phase were found to be dependent on the chirp repetition rate across the scalp, but independent of repetition rate in the ears. The normal drop in ASSR SNR for high repetition rates seen for click and pure tone stimuli was not found for chirp stimuli. Instead, the ASSR SNRs for chirp stimuli at high repetition rates (95-198 Hz) were found to be comparable to that found at 40 Hz for scalp EEG and even higher than 40 Hz ASSR for ear-EEG.

CONCLUSION

Based on the results, use of chirp stimuli with high repetition rates (95-198 Hz) is advantageous for multiple stimulus ASSR recording in both clinical practice and mobile real-life applications.

Investigation of low dimensional feature spaces for automatic sleep staging

BACKGROUND

Automatic sleep stage classification depends crucially on the selection of a good set of descriptive features. However, the selection of a feature set with an appropriate low computational cost without compromising classification performance is still a challenge. This study attempts to represent sleep EEG patterns using a minimum number of features, without significant performance loss.

METHODS

Three feature selection algorithms were applied to a high dimensional feature space comprising 84 features. These methods were based on a bootstrapping approach guided by Gini ranking and mutual information between the features. The algorithms were tested on three scalp electroencephalography (EEG) and one ear-EEG datasets. The relationship between the information carried by different features was investigated using mutual information and illustrated by a graphical clustering tool.

RESULTS

The minimum number of features that can represent the whole feature set without performance loss was found to range between 5 and 11 for different datasets. In ear-EEG, 7 features based on Continuous Wavelet Transform (CWT) resulted in similar performance as the whole set whereas in two scalp EEG datasets, the difference between minimal CWT set and the whole set was statistically significant (0.008 and 0.017 difference in average kappa). Features were divided into groups according to the type of information they carry. The group containing relative power features was identified as the most informative feature group in sleep stage classification, whereas the group containing non-linear features was found to be the least informative.

CONCLUSIONS

This study showed that EEG sleep staging can be performed based on a low dimensional feature space without significant decrease in sleep staging performance. This is especially important in the case of wearable devices like ear-EEG where low computational complexity is needed. The division of the feature space into groups of features, and the analysis of the distribution of feature groups for different feature set sizes, is helpful in the selection of an appropriate feature set.

272 Long-term monitoring of trait-like characteristics of the sleep electroencephalogram using ear-EEG

Introduction

Wearable electroencephalogram (EEG) monitoring has a remarkable potential, it is safe, scalable and can track neural signatures for long periods. One such signature is the power spectra of non-rapid-eye-movement (NREM) sleep which has been shown to demonstrate a trait-like characteristic. Changes in personalized signatures has been associated with biomarkers of Alzheimer’s disease and is of great interest for early detection and clinical management. This work investigates monitoring of signatures using a wearable device that records EEG from the ear (ear-EEG) and compares the intra- and inter-individual similarity of the neural signatures with that from central scalp-EEG.

Methods

We initiated a two phased in-home study, monitoring 20 subjects for 4 nights (A), followed by a delayed but continued monitoring of 10 subjects for 12 nights (B). In A, subjects wore a dry-electrode ear-EEG system and a partial PSG, in B the subjects wore only the ear-EEG system. Subjects were instructed to follow their usual time schedule and lifestyle. Sleep stages were scored manually according to AASM in A and automatically in B. The grand average power spectra of NREM2 sleep were computed and log-transformed prior to calculating the cosine similarity for determination of the intra- and inter-individual similarity.

Results

The ear-EEG and scalp-EEG analysis showed that mean intra-individual similarity was higher than mean inter-individual similarity. Permutation tests indicate that the observed mean difference is statistically significant p&lt;0.01 for both montages. Comparing the distributions of intra-individual similarities for ear-EEG and scalp-EEG, the observed mean difference is statistically significant p&lt;0.05, in favor of a more stable ear-EEG signature. Comparing ear-EEG signatures between A and B, considering nights from A as reference, all subjects from B were most similar with its own reference signature. Considering signatures from individual nights the accuracy paring subjects from A and B were 88% correct.

Conclusion

Nocturnal ear-EEG measures trait-like characteristics as reliable as scalp-EEG. The neural signature is stable over time within healthy subjects and demonstrated its ability as a personalized signature.

Support (if any):

Droplet-Based Techniques for Printing of Functional Inks for Flexible Physical Sensors

Printed electronics (PE) is an emerging technology that uses functional inks to print electrical components and circuits on variety of substrates. This technology has opened up new possibilities to fabricate flexible, bendable, and form-fitting devices at low-cost and fast speed. There are different printing technologies in use, among which droplet-based techniques are of great interest as they provide the possibility of printing computer-controlled design patterns with high resolution, and greater production flexibility. Nanomaterial inks form the heart of this technology, enabling different functionalities. To this end, intensive research has been carried out on formulating inks with conductive, semiconductive, magnetic, piezoresistive, and piezoelectric properties. Here, a detailed landscape view on different droplet-based printing technologies (inkjet, aerosol jet, and electrohydrodynamic jet) is provided, with comprehensive discussion on their working principals. This is followed by a detailed research overview of different functional inks (metal, carbon, polymer, and ceramic). Different sintering methods and common substrates being used in printed electronics are also discussed, followed by an in-depth review of different physical sensors fabricated by droplet-based techniques. Finally, the challenges facing the field are considered and a perspective on possible ways to overcome them is provided.

EEGs Vary Less Between Lab and Home Locations Than They Do Between People

Given the rapid development of light weight EEG devices which we have witnessed the past decade, it is reasonable to ask to which extent neuroscience could now be taken outside the lab. In this study, we have designed an EEG paradigm well suited for deployment “in the wild.” The paradigm is tested in repeated recordings on 20 subjects, on eight different occasions (4 in the laboratory, 4 in the subject's own home). By calculating the inter subject, intra subject and inter location variance, we find that the inter location variation for this paradigm is considerably less than the inter subject variation. We believe the paradigm is representative of a large group of other relevant paradigms. This means that given the positive results in this study, we find that if a research paradigm would benefit from being performed in less controlled environments, we expect limited problems in doing so.

Ear-EEG for sleep assessment: a comparison with actigraphy and PSG

PURPOSE

To assess automatic sleep staging of three ear-EEG setups with different electrode configurations and compare performance with concurrent polysomnography and wrist-worn actigraphy recordings.

METHODS

Automatic sleep staging was performed for single-ear, single-ear with ipsilateral mastoid, and cross-ear electrode configurations, and for actigraphy data. The polysomnography data were manually scored and used as the gold standard. The automatic sleep staging was tested on 80 full-night recordings from 20 healthy subjects. The scoring performance and sleep metrics were determined for all ear-EEG setups and the actigraphy device.

RESULTS

The single-ear, the single-ear with ipsilateral mastoid setup, and the cross-ear setup performed five class sleep staging with kappa values 0.36, 0.63, and 0.72, respectively. For the single-ear with mastoid electrode and the cross-ear setup, the performance of the sleep metrics, in terms of mean absolute error, was better than the sleep metrics estimated from the actigraphy device in the current study, and also better than current state-of-the-art actigraphy studies.

CONCLUSION

A statistically significant improvement in both accuracy and kappa was observed from single-ear to single-ear with ipsilateral mastoid, and from single-ear with ipsilateral mastoid to cross-ear configurations for both two and five-sleep stage classification. In terms of sleep metrics, the results were more heterogeneous, but in general, actigraphy and single-ear with ipsilateral mastoid configuration were better than the single-ear configuration; and the cross-ear configuration was consistently better than both the actigraphy device and the single-ear configuration.

Predicting Sleep Classification Performance without Labels

When generating automatic sleep reports with mobile sleep monitoring devices, it is crucial to have a good grasp of the reliability of the result. In this paper, we feed features derived from the output of a sleep scoring algorithm to a 'regression ensemble' to estimate the quality of the automatic sleep scoring. We compare this estimate to the actual quality, calculated using a manual scoring of a concurrent polysomnography recording. We find that it is generally possible to estimate the quality of a sleep scoring, but with some uncertainty ('root mean squared error' between estimated and true Cohen's kappa is 0.078). We expect that this method could be useful in situations with many scored nights from the same subject, where an overall picture of scoring quality is needed, but where uncertainty on single nights is less of an issue.

Muscle Activity Detection during Sleep by Ear-EEG

Muscle activation during sleep is an important biomarker in the diagnosis of several sleep disorders and neurodegenerative diseases. Muscle activity is typically assessed manually based on the EMG channels from polysomnography recordings. Ear-EEG provides a mobile and comfortable alternative for sleep assessment. In this study, ear-EEG was used to automatically detect muscle activities during sleep. The study was based on a dataset comprising four full night recordings from 20 healthy subjects with concurrent polysomnography and ear-EEG. A binary label, active or relax, extracted from the chin EMG was assigned to selected 30 s epoch of the sleep recordings in order to train a classifier to predict muscle activation. We found that the ear-EEG based classifier detected muscle activity with an accuracy of 88% and a Cohen's kappa value of 0.71 relative to the labels derived from the chin EMG channels. The analysis also showed a significant difference in the distribution of muscle activity between REM and non-REM sleep.

EEG Headset Evaluation for Detection of Single-Trial Movement Intention for Brain-Computer Interfaces

Brain-computer interfaces (BCIs) can be used in neurorehabilitation; however, the literature about transferring the technology to rehabilitation clinics is limited. A key component of a BCI is the headset, for which several options are available. The aim of this study was to test four commercially available headsets' ability to record and classify movement intentions (movement-related cortical potentials-MRCPs). Twelve healthy participants performed 100 movements, while continuous EEG was recorded from the headsets on two different days to establish the reliability of the measures: classification accuracies of single-trials, number of rejected epochs, and signal-to-noise ratio. MRCPs could be recorded with the headsets covering the motor cortex, and they obtained the best classification accuracies (73%-77%). The reliability was moderate to good for the best headset (a gel-based headset covering the motor cortex). The results demonstrate that, among the evaluated headsets, reliable recordings of MRCPs require channels located close to the motor cortex and potentially a gel-based headset.

Cortical Response to Fat Taste

We sense fat by its texture and smell, but it is still unknown whether we also taste fat despite evidence of both candidate receptors and distinct fat taste sensations. One major reason fat is still not recognized as a basic taste quality is that we first need to demonstrate its underlying neural activity. To investigate such neural fat taste activation, we recorded evoked responses to commercial cow milk products with 0.1%, 4%, and 38 % fat via high-density electroencephalography (EEG) from 24 human participants. The experimental design ensured that the products would only be discriminable via their potential fat taste; all stimuli were carefully controlled for differences in viscosity, lubrication, odor, temperature, and confounding tastes (sweetness, acidity, and “off-taste”) and were delivered directly onto the tongue using a set of computer-controlled syringe pumps. Advanced topographical pattern analysis revealed different neural activation to the milk products 85–134 ms after stimulus onset, which, as expected, best discriminated the two milk fat extremes (0.1% and 38% fat). Notably, this time period has previously been shown to also encode basic taste qualities, such as sweet or salty. By adding to the evidence of cortical fat taste processing in response to staple food, our finding not only substantiates that we taste fat but also highlights its potential relevance during our everyday lives with possible large-scale impacts on motivational eating behavior to explain overconsumption of energy-dense foods.

Evaluation of EEG Headset Mounting for Brain-Computer Interface-Based Stroke Rehabilitation by Patients, Therapists, and Relatives

Brain-computer interfaces (BCIs) have successfully been used for motor recovery training in stroke patients. However, the setup of BCI systems is complex and may be divided into (1) mounting the headset and (2) calibration of the BCI. One of the major problems is mounting the headset for recording brain activity in a stroke rehabilitation context, and usability testing of this is limited. In this study, the aim was to compare the translational aspects of mounting five different commercially available headsets from a user perspective and investigate the design considerations associated with technology transfer to rehabilitation clinics and home use. No EEG signals were recorded, so the effectiveness of the systems have not been evaluated. Three out of five headsets covered the motor cortex which is needed to pick up movement intentions of attempted movements. The other two were as control and reference for potential design considerations. As primary stakeholders, nine stroke patients, eight therapists and two relatives participated; the stroke patients mounted the headsets themselves. The setup time was recorded, and participants filled in questionnaires related to comfort, aesthetics, setup complexity, overall satisfaction, and general design considerations. The patients had difficulties in mounting all headsets except for a headband with a dry electrode located on the forehead (control). The therapists and relatives were able to mount all headsets. The fastest headset to mount was the headband, and the most preferred headsets were the headband and a behind-ear headset (control). The most preferred headset that covered the motor cortex used water-based electrodes. The patients reported that it was important that they could mount the headset themselves for them to use it every day at home. These results have implications for design considerations for the development of BCI systems to be used in rehabilitation clinics and in the patient’s home.

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.

Generic Dry-Contact Ear-EEG

Generic dry-contact ear-EEG allows for discreet, user-friendly, unobtrusive, cost-effective and convenient recordings of EEG in real-life settings. In this study we introduce a new generic earpiece design with larger internal ear electrode distances, resulting in an increased spatial coverage compared to previous generic earpiece designs. The signal quality of ear-Fpz, within-ear (the measuring and reference electrode located in the same ear) and cross-ear (the measuring electrodes located in one ear and the reference electrode in the opposite ear) electrode configurations of the developed generic earpiece was evaluated with auditory steady-state responses (ASSR) and compared to dry-contact cEEGrid. Ten subjects with different ear sizes were included. The recordings were performed in a sleep setup, where the subjects were lying on a bed and the effect of sleeping position (back vs. sides) was investigated. We found that the generic earpiece attained statistically significant ASSRs with ear-Fpz, within-ear and cross-ear electrode configurations. However, the dry-contact cEEGrid achieved significantly higher average ASSR signal-to-noise ratio (SNR) compared to the generic earpiece. Additionally, this study showed no significant difference between back and side positions for the ear-EEG.

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.

Automatic sleep stage classification based on subcutaneous EEG in patients with epilepsy

Background

The interplay between sleep structure and seizure probability has previously been studied using electroencephalography (EEG). Combining sleep assessment and detection of epileptic activity in ultralong-term EEG could potentially optimize seizure treatment and sleep quality of patients with epilepsy. However, the current gold standard polysomnography (PSG) limits sleep recording to a few nights. A novel subcutaneous device was developed to record ultralong-term EEG, and has been shown to measure events of clinical relevance for patients with epilepsy. We investigated whether subcutaneous EEG recordings can also be used to automatically assess the sleep architecture of epilepsy patients.

Method

Four adult inpatients with probable or definite temporal lobe epilepsy were monitored simultaneously with long-term video scalp EEG (LTV EEG) and subcutaneous EEG. In total, 11 nights with concurrent recordings were obtained. The sleep EEG in the two modalities was scored independently by a trained expert according to the American Academy of Sleep Medicine (AASM) rules. By using the sleep stage labels from the LTV EEG as ground truth, an automatic sleep stage classifier based on 30 descriptive features computed from the subcutaneous EEG was trained and tested.

Results

An average Cohen’s kappa of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa = 0.78\pm 0.02$$\end{document}κ=0.78±0.02 was achieved using patient specific leave-one-night-out cross validation. When merging all sleep stages into a single class and thereby evaluating an awake–sleep classifier, we achieved a sensitivity of 94.8% and a specificity of 96.6%. Compared to manually labeled video-EEG, the model underestimated total sleep time and sleep efficiency by 8.6 and 1.8 min, respectively, and overestimated wakefulness after sleep onset by 13.6 min.

Conclusion

This proof-of-concept study shows that it is possible to automatically sleep score patients with epilepsy based on two-channel subcutaneous EEG. The results are comparable with the methods currently used in clinical practice. In contrast to comparable studies with wearable EEG devices, several nights were recorded per patient, allowing for the training of patient specific algorithms that can account for the individual brain dynamics of each patient. Clinical trial registered at ClinicalTrial.gov on 19 October 2016 (ID:NCT02946151).

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.

EEG discrimination of perceptually similar tastes

Perceptually similar stimuli, despite not being consciously distinguishable, may result in distinct cortical brain activations. Hypothesizing that perceptually similar tastes are discriminable by electroencephalography (EEG), we recorded 22 human participants' response to equally intense sweet-tasting stimuli: caloric sucrose, low-caloric aspartame, and a low-caloric mixture of aspartame and acesulfame K. Time-resolved multivariate pattern analysis of the 128-channel EEG was used to discriminate the taste responses at single-trial level. Supplementing the EEG study, we also performed a behavioral study to assess the participants' perceptual ability to discriminate the taste stimuli by a triangle test of all three taste pair combinations. The three taste stimuli were found to be perceptually similar or identical in the behavioral study, yet discriminable from 0.08 to 0.18 s by EEG analysis. Comparing the participants' responses in the EEG and behavioral study, we found that brain responses to perceptually similar tastes are discriminable, and we also found evidence suggesting that perceptually identical tastes are discriminable by the brain. Moreover, discriminability of brain responses was related to individual participants' perceptual ability to discriminate the tastes. We did not observe a relation between brain response discriminability and calorie content of the taste stimuli. Thus, besides demonstrating discriminability of perceptually similar and identical tastes with EEG, we also provide the first proof of a functional relation between brain response and perception of taste stimuli at individual level.

Toward EEG-Assisted Hearing Aids: Objective Threshold Estimation Based on Ear-EEG in Subjects With Sensorineural Hearing Loss

Electrophysiological feedback on activity in the auditory pathway may potentially advance the next generation of hearing aids. Conventional electroencephalographic (EEG) systems are, however, impractical during daily life and incompatible with hearing aids. Ear-EEG is a method in which the EEG is recorded from electrodes embedded in a hearing aid like earpiece. The method therefore provides an unobtrusive way of measuring neural activity suitable for use in everyday life. This study aimed to determine whether ear-EEG could be used to estimate hearing thresholds in subjects with sensorineural hearing loss. Specifically, ear-EEG was used to determine physiological thresholds at 0.5, 1, 2, and 4 kHz using auditory steady-state response measurements. To evaluate ear-EEG in relation to current methods, thresholds were estimated from a concurrently recorded conventional scalp EEG. The threshold detection rate for ear-EEG was 20% lower than the detection rate for scalp EEG. Thresholds estimated using in-ear referenced ear-EEG were found to be elevated at an average of 5.9, 2.3, 5.6, and 1.5 dB relative to scalp thresholds at 0.5, 1, 2, and 4 kHz, respectively. No differences were found in the variance of means between in-ear ear-EEG and scalp EEG. In-ear ear-EEG, auditory steady-state response thresholds were found at 12.1 to 14.4 dB sensation level with an intersubject variation comparable to that of behavioral thresholds. Collectively, it is concluded that although further refinement of the method is needed to optimize the threshold detection rate, ear-EEG is a feasible method for hearing threshold level estimation in subjects with sensorineural hearing impairment.

Auditory Steady-State Responses Across Chirp Repetition Rates For Ear-EEG And Scalp EEG

Measurement of auditory steady-state responses (ASSR) using ear-EEG potentially enables objective audiometry out of the clinic in the everyday life of hearing aid users. As ear-EEG are measured from electrodes placed within the ear, electrode distances are inherently small and consequently the potential differences, and thereby signal amplitudes, are also small. Because the detection of the ASSR is based on the signalto-noise ratio (SNR), it is of fundamental interest to know the inherent SNR of the ASSR as a function of the stimulus repetition rate. In this study, ASSRs were recorded using both scalp and ear-EEG in response to broadband chirp stimuli with repetition rates from 20 to 95 Hz. The results showed that in general ear-EEG and scalp EEG SNR was on par across repetition rates; an exception to this was at rates around 40 Hz where the SNR was significantly lower for ear-EEG as compared to scalp EEG. For ear-EEG, the ASSR was relatively constant across repetition rates, whereas the noise showed a 1/f characteristic. In consequence, there was a tendency to increased SNR as a function of repetition rate. This suggests that use of relatively high repetition rates may be beneficial in earEEG applications.

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.

Automatic sleep staging using ear-EEG

Background

Sleep and sleep quality assessment by means of sleep stage analysis is important for both scientific and clinical applications. Unfortunately, the presently preferred method, polysomnography (PSG), requires considerable expert assistance and significantly affects the sleep of the person under observation. A reliable, accurate and mobile alternative to the PSG would make sleep information much more readily available in a wide range of medical circumstances.

New method

Using an already proven method, ear-EEG, in which electrodes are placed inside the concha and ear canal, we measure cerebral activity and automatically score the sleep into up to five stages. These results are compared to manual scoring by trained clinicians, based on a simultaneously recorded PSG.

Results

The correspondence between manually scored sleep, based on the PSG, and the automatic labelling, based on ear-EEG data, was evaluated using Cohen’s kappa coefficient. Kappa values are in the range 0.5–0.8, making ear-EEG relevant for both scientific and clinical applications. Furthermore, a sleep-wake classifier with leave-one-out cross validation yielded specificity of 0.94 and sensitivity of 0.52 for the sleep stage.

Comparison with existing method(s)

Ear-EEG based scoring has clear advantages when compared to both the PSG and other mobile solutions, such as actigraphs. It is far more mobile, and potentially cheaper than the PSG, and the information on sleep stages is far superior to a wrist-based actigraph, or other devices based solely on body movement.

Conclusions

This study shows that ear-EEG recordings carry information about sleep stages, and indicates that automatic sleep staging based on ear-EEG can classify sleep stages with a level of accuracy that makes it relevant for both scientific and clinical sleep assessment.

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.

Ear-EEG-Based Objective Hearing Threshold Estimation Evaluated on Normal Hearing Subjects

OBJECTIVE

Hearing threshold levels have been estimated successfully in the clinic using the objective electroencephalogram (EEG) based technique of auditory steady-state response (ASSR). The recent method of ear-EEG could enable ASSR hearing tests to be performed in everyday life, rather than in a specialized clinic, enabling cheaper and easier monitoring of audiometric thresholds over time. The objective of the current study was to evaluate the feasibility of ear-EEG in audiometric characterization of auditory sensitivity thresholds.

METHODS

An ear-EEG setup was used to estimate ASSR hearing threshold levels to CE-chirp stimuli (with center frequencies 0.5, 1, 2, and 4 kHz) from four different electrode configurations including conventional scalp configuration, ear electrode with scalp reference, ear electrode with reference in the opposite ear and ear electrode with reference in the same ear. To evaluate the ear-EEG setup, ASSR thresholds estimated using ear-EEG were compared to ASSR thresholds estimated using standardized audiological equipment.

RESULTS

The SNRs of in-ear ear-EEG recordings were found to be on average 2.7 to 6.5 dB lower than SNRs of conventional scalp EEG. Thresholds estimated from in-ear referenced ear-EEG were on average 15.0 ± 3.4, 9.1 ± 4.4, 12.5 ± 3.7, and 12.1 ± 2.6 dB above scalp EEG thresholds for 0.5, 1, 2, and 4 kHz, respectively.

CONCLUSION

We demonstrate that hearing threshold levels can be estimated from ear-EEG recordings made from electrodes placed in one ear.

SIGNIFICANCE

Objective hearing threshold estimation based on ear-EEG can be integrated into hearing aids, thereby allowing hearing assessment to be performed by the hearing instrument on a regular basis.

Automatic sleep stage classification using ear-EEG

Sleep assessment is of great importance in the diagnosis and treatment of sleep disorders. In clinical practice this is typically performed based on polysomnography recordings and manual sleep staging by experts. This procedure has the disadvantages that the measurements are cumbersome, may have a negative influence on the sleep, and the clinical assessment is labor intensive. Addressing the latter, there has recently been encouraging progress in the field of automatic sleep staging [1]. Furthermore, a minimally obtrusive method for recording EEG from electrodes in the ear (ear-EEG) has recently been proposed [2]. The objective of this study was to investigate the feasibility of automatic sleep stage classification based on ear-EEG. This paper presents a preliminary study based on recordings from a total of 18 subjects. Sleep scoring was performed by a clinical expert based on frontal, central and occipital region EEG, as well as EOG and EMG. 5 subjects were excluded from the study because of alpha wave contamination. In one subject the standard polysomnography was supplemented by ear-EEG. A single EEG channel sleep stage classifier was implemented using the same features and the same classifier as proposed in [1]. The performance of the single channel sleep classifier based on the scalp recordings showed an 85.7 % agreement with the manual expert scoring through 10-fold inter-subject cross validation, while the performance of the ear-EEG recordings was based on a 10-fold intra-subject cross validation and showed an 82 % agreement with the manual scoring. These results suggest that automatic sleep stage classification based on ear-EEG recordings may provide similar performance as compared to single channel scalp EEG sleep stage classification. Thereby ear-EEG may be a feasible technology for future minimal intrusive sleep stage classification.

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.

On the Keyhole Hypothesis: High Mutual Information between Ear and Scalp EEG

We propose and test the keyhole hypothesis—that measurements from low dimensional EEG, such as ear-EEG reflect a broadly distributed set of neural processes. We formulate the keyhole hypothesis in information theoretical terms. The experimental investigation is based on legacy data consisting of 10 subjects exposed to a battery of stimuli, including alpha-attenuation, auditory onset, and mismatch-negativity responses and a new medium-long EEG experiment involving data acquisition during 13 h. Linear models were estimated to lower bound the scalp-to-ear capacity, i.e., predicting ear-EEG data from simultaneously recorded scalp EEG. A cross-validation procedure was employed to ensure unbiased estimates. We present several pieces of evidence in support of the keyhole hypothesis: There is a high mutual information between data acquired at scalp electrodes and through the ear-EEG “keyhole,” furthermore we show that the view—represented as a linear mapping—is stable across both time and mental states. Specifically, we find that ear-EEG data can be predicted reliably from scalp EEG. We also address the reverse view, and demonstrate that large portions of the scalp EEG can be predicted from ear-EEG, with the highest predictability achieved in the temporal regions and when using ear-EEG electrodes with a common reference electrode.

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.

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.

Case comparison of sleep features from ear-EEG and scalp-EEG

Background

We investigate the potential usability of a novel in-the-ear electroencephalography recording device for sleep staging.

Methods

In one healthy subject we compare simultaneous earelectroencephalography to standard scalp EEG visually and using power spectrograms. Hypnograms independently derived from the records are compared.

Results

We find that alpha activity, K complexes, sleep spindles and slow wave sleep can be visually distinguished using earelectroencephalography. Spectral peaks are shared between the two records. Hypnograms are 90.9% similar.

Conclusion

The results indicate that ear-electroencephalography can be used for sleep staging.

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.

Developing an online steady-state visual evoked potential-based brain-computer interface system using EarEEG

The purpose of this study is to demonstrate an online steady-state visual evoked potential (SSVEP)-based BCI system using EarEEG. EarEEG is a novel recording concept where electrodes are embedded on the surface of earpieces customized to the individual anatomical shape of users' ear. It has been shown that the EarEEG can be used to record SSVEPs in previous studies. However, a long distance between the visual cortex and the ear makes the signal-to-noise ratio (SNR) of SSVEPs acquired by the EarEEG relatively low. Recently, filter bank- and training data-based canonical correlation analysis algorithms have shown significant performance improvement in terms of accuracy of target detection and information transfer rate (ITR). This study implemented an online four-class SSVEP-based BCI system using EarEEG. Four subjects participated in offline and online BCI experiments. For the offline classification, an average accuracy of 82.71±11.83 % was obtained using 4 sec-long SSVEPs acquired from earpieces. In the online experiment, all subjects successfully completed the tasks with an average accuracy of 87.92±12.10 %, leading to an average ITR of 16.60±6.55 bits/min. The results suggest that EarEEG can be used to perform practical BCI applications. The EarEEG has the potential to be used as a portable EEG recordings platform, that could enable real-world BCI applications.

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.