ear-EEG Recordings Research Articles

Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors.

EEG-enabled earbuds represent a promising frontier in brain activity monitoring beyond traditional laboratory testing. Their discrete form factor and proximity to the brain make them the ideal candidate for the first generation of discrete non-invasive brain-computer interfaces (BCIs). However, this new technology will require comprehensive characterization before we see widespread consumer and health-related usage. To address this need, we developed a validation toolkit that aims to facilitate and expand the assessment of ear-EEG devices. The first component of this toolkit is a desktop application ("EaR-P Lab") that controls several EEG validation paradigms. This application uses the Lab Streaming Layer (LSL) protocol, making it compatible with most current EEG systems. The second element of the toolkit introduces an adaptation of the phantom evaluation concept to the domain of ear-EEGs. Specifically, it utilizes 3D scans of the test subjects' ears to simulate typical EEG activity around and inside the ear, allowing for controlled assessment of different ear-EEG form factors and sensor configurations. Each of the EEG paradigms were validated using wet-electrode ear-EEG recordings and benchmarked against scalp-EEG measurements. The ear-EEG phantom was successful in acquiring performance metrics for hardware characterization, revealing differences in performance based on electrode location. This information was leveraged to optimize the electrode reference configuration, resulting in increased auditory steady-state response (ASSR) power. Through this work, an ear-EEG evaluation toolkit is made available with the intention to facilitate the systematic assessment of novel ear-EEG devices from hardware to neural signal acquisition.

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

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. 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. 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. 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.

Subclinical Epileptiform Activity in Dementia with Lewy Bodies.

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

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

BackgroundIn 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). MethodsIn 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. ResultsThe 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. ConclusionsLong-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.

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

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

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.

Building an Ear-EEG System by Hacking a Commercial Neck Speaker and a Commercial EEG Amplifier to Record Brain Activity Beyond the Lab

Electroencephalography (EEG) allows measuring human brain activity for scientific, diagnostic and therapeutic purposes. For scientific purposes, EEG is traditionally used to study the brain-behaviour relationship under well controlled laboratory conditions while the participant sits quietly in a room. Consequently, our knowledge regarding the brain-behaviour relationship was limited to human behaviour under constrained conditions. The development of mobile EEG hardware in the last years allows moving beyond the lab and studying brain activity in everyday life. Most EEG systems record from electrodes that are distributed over the head, kept in place with an elastic cap or net. For long term EEG recordings, to study natural behaviour in public, these systems are not optimal, as they are rather bulky and unpleasant to wear for longer periods. We argue for “transparent”, ear-centered EEG systems instead. Transparent in the sense that both the person wearing the EEG system as well as the people interacting with that person are not disturbed by it. We describe here the integration of a commercially available research-grade mobile EEG amplifier and a commercially available neck speaker as a mounting system to be used in combination with ear electrodes (cEEGrid). This nEEGlace can be used for ear-EEG recordings in public over extended periods of time. The integrated microphone and the integrated speakers provide the possibility to play and record sound. This allows conducting experiments on auditory perception beyond the lab by both studying the brain responses to naturally occurring sounds as well as to experimental auditory stimuli. We describe the general design, present a working prototype, outline a number of use cases and discuss future improvements of the proposed design.

Ear-EEG-based sleep scoring in epilepsy: A comparison with scalp-EEG.

Ear-EEG is a wearable electroencephalogram-recording device. It relies on recording electrodes that are nested within a custom-fitted earpiece in the external ear canal. The concept has previously been tested for seizure detection in epileptic patients and for sleep recordings in a healthy population. This study is the first to examine the use of ear-EEG recordings for sleep staging in patients with epilepsy, comparing it with standard recordings from scalp-EEG. We use individuals with epilepsy because of their multiple sleep disturbances, and their complex relationship between seizures and sleep, which make this group very likely to benefit from wearable electroencephalogram devices for sleep if it were introduced in the clinic. The accuracy of the ear-EEG against that of the scalp-EEG is compared for sleep staging, and we evaluate features of sleep architecture in individuals with epilepsy. A mean kappa value of 0.74 is found for the agreement between hypnograms derived from ear-EEG and scalp-EEG. Furthermore, it was discovered that sleep stage transition frequency could be contributing to the kappa variation. These findings are related to other ear-recording systems in the literature, and the potentials and future obstacles of the device are discussed.

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.

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.

Dry-Contact Electrode Ear-EEG.

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. 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. 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. The study demonstrated that dry-contact electrode ear-EEG is a feasible technology for EEG recording. 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.

Automatic sleep staging using ear-EEG

BackgroundSleep 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 methodUsing 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.ResultsThe 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.ConclusionsThis 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.

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

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

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.

Automatic Sleep Monitoring Using Ear-EEG.

The monitoring of sleep patterns without patient’s inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording the electroencephalogram (ear-EEG). The selected features for sleep pattern classification from a single ear-EEG channel include the spectral edge frequency and multi-scale fuzzy entropy, a structural complexity feature. In this preliminary study, the manually scored hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects are used as labels for two analysis scenarios: 1) classification of ear-EEG hypnogram labels from ear-EEG recordings; and 2) prediction of scalp-EEG hypnogram labels from ear-EEG recordings. We consider both 2-class and 4-class sleep scoring, with the achieved accuracies ranging from 78.5% to 95.2% for ear-EEG labels predicted from ear-EEG, and 76.8% to 91.8% for scalp-EEG labels predicted from ear-EEG. The corresponding Kappa coefficients range from 0.64 to 0.83 for Scenario 1, and indicate substantial to almost perfect agreement, while for Scenario 2 the range of 0.65–0.80 indicates substantial agreement, thus further supporting the feasibility of in-ear sensing for sleep monitoring in the community.

Target Speaker Detection with Concealed EEG Around the Ear.

Target speaker identification is essential for speech enhancement algorithms in assistive devices aimed toward helping the hearing impaired. Several recent studies have reported that target speaker identification is possible through electroencephalography (EEG) recordings. If the EEG system could be reduced to acceptable size while retaining the signal quality, hearing aids could benefit from the integration with concealed EEG. To compare the performance of a multichannel around-the-ear EEG system with high-density cap EEG recordings an envelope tracking algorithm was applied in a competitive speaker paradigm. The data from 20 normal hearing listeners were concurrently collected from the traditional state-of-the-art laboratory wired EEG system and a wireless mobile EEG system with two bilaterally-placed around-the-ear electrode arrays (cEEGrids). The results show that the cEEGrid ear-EEG technology captured neural signals that allowed the identification of the attended speaker above chance-level, with 69.3% accuracy, while cap-EEG signals resulted in the accuracy of 84.8%. Further analyses investigated the influence of ear-EEG signal quality and revealed that the envelope tracking procedure was unaffected by variability in channel impedances. We conclude that the quality of concealed ear-EEG recordings as acquired with the cEEGrid array has potential to be used in the brain-computer interface steering of hearing aids.

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.