Concealed Around-the-Ear EEG Captures Cognitive Processing in a Visual Simon Task

Earable Multimodal Sensing and Stimulation: A Prospective Towards Unobtrusive Closed-Loop Biofeedback

The human ear has emerged as a bidirectional gateway to the brain's and body's signals. Recent advances in around-the-ear and in-ear sensors have enabled the assessment of biomarkers and physiomarkers derived from brain and cardiac activity using ear-electroencephalography (ear-EEG), photoplethysmography (ear-PPG), and chemical sensing of analytes from the ear, with ear-EEG having been taken beyond-the-lab to outer space. Parallel advances in non-invasive and minimally invasive brain stimulation techniques have leveraged the ear's access to two cranial nerves to modulate brain and body activity. The vestibulocochlear nerve stimulates the auditory cortex and limbic system with sound, while the auricular branch of the vagus nerve indirectly but significantly couples to the autonomic nervous system and cardiac output. Acoustic and current mode stimuli delivered using discreet and unobtrusive earables are an active area of research, aiming to make biofeedback and bioelectronic medicine deliverable outside of the clinic, with remote and continuous monitoring of therapeutic responsivity and long-term adaptation. Leveraging recent advances in ear-EEG, transcutaneous auricular vagus nerve stimulation (taVNS), and unobtrusive acoustic stimulation, we review accumulating evidence that combines their potential into an integrated earable platform for closed-loop multimodal sensing and neuromodulation, towards personalized and holistic therapies that are near, in- and around-the-ear.

Detection of Movement-Related Brain Activity Associated with Hand and Tongue Movements from Single-Trial Around-Ear EEG

Movement intentions of motor impaired individuals can be detected in laboratory settings via electroencephalography Brain-Computer Interfaces (EEG-BCIs) and used for motor rehabilitation and external system control. The real-world BCI use is limited by the costly, time-consuming, obtrusive, and uncomfortable setup of scalp EEG. Ear-EEG offers a faster, more convenient, and more aesthetic setup for recording EEG, but previous work using expensive amplifiers detected motor intentions at chance level. This study investigates the feasibility of a low-cost ear-EEG BCI for the detection of tongue and hand movements for rehabilitation and control purposes. In this study, ten able-bodied participants performed 100 right wrist extensions and 100 tongue-palate movements while three channels of EEG were recorded around the left ear. Offline movement vs. idle activity classification of ear-EEG was performed using temporal and spectral features classified with Random Forest, Support Vector Machine, K-Nearest Neighbours, and Linear Discriminant Analysis in three scenarios: Hand (rehabilitation purpose), hand (control purpose), and tongue (control purpose). The classification accuracies reached 70%, 73%, and 83%, respectively, which was significantly higher than chance level. These results suggest that a low-cost ear-EEG BCI can detect movement intentions for rehabilitation and control purposes. Future studies should include online BCI use with the intended user group in real-life settings.

Conductive Hydrogel Tapes for Tripolar EEG: A Promising Solution to Paste-Related Challenges

Electroencephalography (EEG) remains pivotal in neuroscience for its non-invasive exploration of brain activity, yet traditional electrodes are plagued with artifacts and the application of conductive paste poses practical challenges. Tripolar concentric ring electrode (TCRE) sensors used for EEG (tEEG) attenuate artifacts automatically, improving the signal quality. Hydrogel tapes offer a promising alternative to conductive paste, providing mess-free application and reliable electrode-skin contact in locations without hair. Since the electrodes of the TCRE sensors are only 1.0 mm apart, the impedance of the skin-to-electrode impedance-matching medium is critical. This study evaluates four hydrogel tapes' efficacies in EEG electrode application, comparing impedance and alpha wave characteristics. Healthy adult participants underwent tEEG recordings using different tapes. The results highlight varying impedances and successful alpha wave detection despite increased tape-induced impedance. MATLAB's EEGLab facilitated signal processing. This study underscores hydrogel tapes' potential as a convenient and effective alternative to traditional paste, enriching tEEG research methodologies. Two of the conductive hydrogel tapes had significantly higher alpha wave power than the other tapes, but were never significantly lower.

Recording the tactile P300 with the cEEGrid for potential use in a brain-computer interface

Brain-computer interfaces (BCIs) are scientifically well established, but they rarely arrive in the daily lives of potential end-users. This could be in part because electroencephalography (EEG), a prevalent method to acquire brain activity for BCI operation, is considered too impractical to be applied in daily life of end-users with physical impairment as an assistive device. Hence, miniaturized EEG systems such as the cEEGrid have been developed. While they promise to be a step toward bridging the gap between BCI development, lab demonstrations, and home use, they still require further validation. Encouragingly, the cEEGrid has already demonstrated its ability to record visually and auditorily evoked event-related potentials (ERP), which are important as input signal for many BCIs. With this study, we aimed at evaluating the cEEGrid in the context of a BCI based on tactually evoked ERPs. To compare the cEEGrid with a conventional scalp EEG, we recorded brain activity with both systems simultaneously. Forty healthy participants were recruited to perform a P300 oddball task based on vibrotactile stimulation at four different positions. This tactile paradigm has been shown to be feasible for BCI repeatedly but has never been tested with the cEEGrid. We found distinct P300 deflections in the cEEGrid data, particularly at vertical bipolar channels. With an average of 63%, the cEEGrid classification accuracy was significantly above the chance level (25%) but significantly lower than the 81% reached with the EEG cap. Likewise, the P300 amplitude was significantly lower (cEEGrid R2-R7: 1.87 μV, Cap Cz: 3.53 μV). These results indicate that a tactile BCI using the cEEGrid could potentially be operated, albeit with lower efficiency. Additionally, participants' somatosensory sensitivity was assessed, but no correlation to the accuracy of either EEG system was shown. Our research contributes to the growing amount of literature comparing the cEEGrid to conventional EEG systems and provides first evidence that the tactile P300 can be recorded behind the ear. A BCI based on a thus simplified EEG system might be more readily accepted by potential end-users, provided the accuracy can be substantially increased, e.g., by training and improved classification.

Evaluation of a headphones-fitted EEG system for the recording of auditory evoked potentials and mental workload assessment

Advancements in portable neuroimaging technologies open up new opportunities to gain insight into the neural dynamics and cognitive processes underlying day-to-day behaviors. In this study, we evaluated the relevance of a headphone- mounted electroencephalogram (EEG) system for monitoring mental workload. The participants (N = 12) were instructed to pay attention to auditory alarms presented sporadically while performing the Multi-Attribute Task Battery (MATB) whose difficulty was staged across three conditions to manipulate mental workload. The P300 Event-Related Potentials (ERP) elicited by the presentation of auditory alarms were used as probes of attentional resources available. The amplitude and latency of P300 ERPs were compared across experimental conditions. Our findings indicate that the P300 ERP component can be captured using a headphone-mounted EEG system. Moreover, neural responses to alarm could be used to classify mental workload with high accuracy (over 80%) at a single-trial level. Our analyses indicated that the signal-to-noise ratio acquired by the sponge-based sensors remained stable throughout the recordings. These results highlight the potential of portable neuroimaging technology for the development of neuroassistive applications while underscoring the current limitations and challenges associated with the integration of EEG sensors in everyday-life wearable technologies. Overall, our study contributes to the growing body of research exploring the feasibility and validity of wearable neuroimaging technologies for the study of human cognition and behavior in real-world settings.

High-density ear-EEG for understanding ear-centered EEG

Background. Mobile ear-EEG provides the opportunity to record EEG unobtrusively in everyday life. However, in real-life, the EEG data quickly becomes difficult to interpret, as the neural signal is contaminated by other, non-neural signal contributions. Due to the small number of electrodes in ear-EEG devices, the interpretation of the EEG becomes even more difficult. For meaningful and reliable ear-EEG, it is crucial that the brain signals we wish to record in real life are well-understood and that we make optimal use of the available electrodes. Their placement should be guided by prior knowledge about the characteristics of the signal of interest.Objective.We want to understand the signal we record with ear-EEG and make recommendations on how to optimally place a limited number of electrodes.Approach.We built a high-density ear-EEG with 31 channels spaced densely around one ear. We used it to record four auditory event-related potentials (ERPs): the mismatch negativity, the P300, the N100 and the N400. With this data, we gain an understanding of how different stages of auditory processing are reflected in ear-EEG. We investigate the electrode configurations that carry the most information and use a mass univariate ERP analysis to identify the optimal channel configuration. We additionally use a multivariate approach to investigate the added value of multi-channel recordings.Main results.We find significant condition differences for all ERPs. The different ERPs vary considerably in their spatial extent and different electrode positions are necessary to optimally capture each component. In the multivariate analysis, we find that the investigation of the ERPs benefits strongly from multi-channel ear-EEG.Significance.Our work emphasizes the importance of a strong theoretical and practical background when building and using ear-EEG. We provide recommendations on finding the optimal electrode positions. These results will guide future research employing ear-EEG in real-life scenarios.

Recent Progress in Wearable Brain-Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review

Importance: Brain-computer interface (BCI) decodes and converts brain signals into machine instructions to interoperate with the external world. However, limited by the implantation risks of invasive BCIs and the operational complexity of conventional noninvasive BCIs, applications of BCIs are mainly used in laboratory or clinical environments, which are not conducive to the daily use of BCI devices. With the increasing demand for intelligent medical care, the development of wearable BCI systems is necessary. Highlights: Based on the scalp-electroencephalogram (EEG), forehead-EEG, and ear-EEG, the state-of-the-art wearable BCI devices for disease management and patient assistance are reviewed. This paper focuses on the EEG acquisition equipment of the novel wearable BCI devices and summarizes the development direction of wearable EEG-based BCI devices. Conclusions: BCI devices play an essential role in the medical field. This review briefly summarizes novel wearable EEG-based BCIs applied in the medical field and the latest progress in related technologies, emphasizing its potential to help doctors, patients, and caregivers better understand and utilize BCI devices.

Assessing focus through ear-EEG: a comparative study between conventional cap EEG and mobile in- and around-the-ear EEG systems

Introduction

As our attention is becoming a commodity that an ever-increasing number of applications are competing for, investing in modern day tools and devices that can detect our mental states and protect them from outside interruptions holds great value. Mental fatigue and distractions are impacting our ability to focus and can cause workplace injuries. Electroencephalography (EEG) may reflect concentration, and if EEG equipment became wearable and inconspicuous, innovative brain-computer interfaces (BCI) could be developed to monitor mental load in daily life situations. The purpose of this study is to investigate the potential of EEG recorded inside and around the human ear to determine levels of attention and focus.

Methods

In this study, mobile and wireless ear-EEG were concurrently recorded with conventional EEG (cap) systems to collect data during tasks related to focus: an N-back task to assess working memory and a mental arithmetic task to assess cognitive workload. The power spectral density (PSD) of the EEG signal was analyzed to isolate consistent differences between mental load conditions and classify epochs using step-wise linear discriminant analysis (swLDA).

Results and discussion

Results revealed that spectral features differed statistically between levels of cognitive load for both tasks. Classification algorithms were tested on spectral features from twelve and two selected channels, for the cap and the ear-EEG. A two-channel ear-EEG model evaluated the performance of two dry in-ear electrodes specifically. Single-trial classification for both tasks revealed above chance-level accuracies for all subjects, with mean accuracies of: 96% (cap-EEG) and 95% (ear-EEG) for the twelve-channel models, 76% (cap-EEG) and 74% (in-ear-EEG) for the two-channel model for the N-back task; and 82% (cap-EEG) and 85% (ear-EEG) for the twelve-channel, 70% (cap-EEG) and 69% (in-ear-EEG) for the two-channel model for the arithmetic task. These results suggest that neural oscillations recorded with ear-EEG can be used to reliably differentiate between levels of cognitive workload and working memory, in particular when multi-channel recordings are available, and could, in the near future, be integrated into wearable devices.

Evaluation of Real-Time Endogenous Brain-Computer Interface Developed Using Ear-Electroencephalography

While previous studies have demonstrated the feasibility of using ear-electroencephalography (ear-EEG) for the development of brain-computer interfaces (BCIs), most of them have been performed using exogenous paradigms in offline environments. To verify the reliable feasibility of constructing ear-EEG-based BCIs, the feasibility of using ear-EEG should be further demonstrated using another BCI paradigm, namely the endogenous paradigm, in real-time online environments. Exogenous and endogenous BCIs are to use the EEG evoked by external stimuli and induced by self-modulation, respectively. In this study, we investigated whether an endogenous ear-EEG-based BCI with reasonable performance can be implemented in online environments that mimic real-world scenarios. To this end, we used three different mental tasks, i.e., mental arithmetic, word association, and mental singing, and performed BCI experiments with fourteen subjects on three different days to investigate not only the reliability of a real-time endogenous ear-EEG-based BCI, but also its test-retest reliability. The mean online classification accuracy was almost 70%, which was equivalent to a marginal accuracy for a practical two-class BCI (70%), demonstrating the feasibility of using ear-EEG for the development of real-time endogenous BCIs, but further studies should follow to improve its performance enough to be used for practical ear-EEG-based BCI applications.

Benchmarking cEEGrid and Solid Gel-Based Electrodes to Classify Inattentional Deafness in a Flight Simulator

Transfer from experiments in the laboratory to real-life tasks is challenging due notably to the inability to reproduce the complexity of multitasking dynamic everyday life situations in a standardized lab condition and to the bulkiness and invasiveness of recording systems preventing participants from moving freely and disturbing the environment. In this study, we used a motion flight simulator to induce inattentional deafness to auditory alarms, a cognitive difficulty arising in complex environments. In addition, we assessed the possibility of two low-density EEG systems a solid gel-based electrode Enobio (Neuroelectrics, Barcelona, Spain) and a gel-based cEEGrid (TMSi, Oldenzaal, Netherlands) to record and classify brain activity associated with inattentional deafness (misses vs. hits to odd sounds) with a small pool of expert participants. In addition to inducing inattentional deafness (missing auditory alarms) at much higher rates than with usual lab tasks (34.7% compared to the usual 5%), we observed typical inattentional deafness-related activity in the time domain but also in the frequency and time-frequency domains with both systems. Finally, a classifier based on Riemannian Geometry principles allowed us to obtain more than 70% of single-trial classification accuracy for both mobile EEG, and up to 71.5% for the cEEGrid (TMSi, Oldenzaal, Netherlands). These results open promising avenues toward detecting cognitive failures in real-life situations, such as real flight.

Detecting impaired language processing in patients with mild cognitive impairment using around-the-ear cEEgrid electrodes

Mild cognitive impairment (MCI) is the term used to identify those individuals with subjective and objective cognitive decline but with preserved activities of daily living and an absence of dementia. Although MCI can impact functioning in different cognitive domains, most notably episodic memory, relatively little is known about the comprehension of language in MCI. In this study, we used around-the-ear electrodes (cEEGrids) to identify impairments during language comprehension in patients with MCI. In a group of 23 patients with MCI and 23 age-matched controls, language comprehension was tested in a two-word phrase paradigm. We examined the oscillatory changes following word onset as a function of lexico-semantic single-word retrieval (e.g., swrfeq vs. swift) and multiword binding processes (e.g., horse preceded by swift vs. preceded by swrfeq). Electrophysiological signatures (as measured by the cEEGrids) were significantly different between patients with MCI and controls. In controls, lexical retrieval was associated with a rebound in the alpha/beta range, and binding was associated with a post-word alpha/beta suppression. In contrast, both the single-word retrieval and multiword binding signatures were absent in the MCI group. The signatures observed using cEEGrids in controls were comparable with those signatures obtained with a full-cap EEG setup. Importantly, our findings suggest that patients with MCI have impaired electrophysiological signatures for comprehending single words and multiword phrases. Moreover, cEEGrid setups provide a noninvasive and sensitive clinical tool for detecting early impairments in language comprehension in MCI.

Measuring Correlates of Mental Workload During Simulated Driving Using cEEGrid Electrodes: A Test-Retest Reliability Analysis

The EEG reflects mental processes, especially modulations in the alpha and theta frequency bands are associated with attention and the allocation of mental resources. EEG has also been used to study mental processes while driving, both in real environments and in virtual reality. However, conventional EEG methods are of limited use outside of controlled laboratory settings. While modern EEG technologies offer hardly any restrictions for the user, they often still have limitations in measurement reliability. We recently showed that low-density EEG methods using film-based round the ear electrodes (cEEGrids) are well-suited to map mental processes while driving a car in a driving simulator. In the present follow-up study, we explored aspects of ecological and internal validity of the cEEGrid measurements. We analyzed longitudinal data of 127 adults, who drove the same driving course in a virtual environment twice at intervals of 12-15 months while the EEG was recorded. Modulations in the alpha and theta frequency bands as well as within behavioral parameters (driving speed and steering wheel angular velocity) which were highly consistent over the two measurement time points were found to reflect the complexity of the driving task. At the intraindividual level, small to moderate (albeit significant) correlations were observed in about 2/3 of the participants, while other participants showed significant deviations between the two measurements. Thus, the test-retest reliability at the intra-individual level was rather low and challenges the value of the application for diagnostic purposes. However, across all participants the reliability and ecological validity of cEEGrid electrodes were satisfactory in the context of driving-related parameters.

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers

Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide a comprehensive survey of the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.

Flex-printed forehead EEG sensors (fEEGrid) for long-term EEG acquisition

OBJECTIVE

In this report we present the fEEGrid, an electrode array applied to the forehead that allows convenient long-term recordings of electroencephalography (EEG) signals over many hours.

APPROACH

Twenty young, healthy participants wore the fEEGrid and completed traditional EEG paradigms in two sessions on the same day. The sessions were eight hours apart, participants performed the same tasks in an early and a late session. For the late session fEEGrid data were concurrently recorded with traditional cap EEG data.

MAIN RESULTS

Our analyses show that typical event-related potentials responses were captured reliably by the fEEGrid. Single-trial analyses revealed that classification was possible above chance level for auditory and tactile oddball paradigms. We also found that the signal quality remained high and impedances did not deteriorate, but instead improved over the course of the day. Regarding wearing comfort, all participants indicated that the fEEGrid was comfortable to wear and did not cause any pain even after 8 h of wearing it.

SIGNIFICANCE

We show in this report, that high quality EEG signals can be captured with the fEEGrid reliably, even in long-term recording scenarios and with a signal quality that may be considered suitable for online brain-computer Interface applications.

Mobile EEG identifies the re-allocation of attention during real-world activity

The distribution of attention between competing processing demands can have dramatic real-world consequences, however little is known about how limited attentional resources are distributed during real-world behaviour. Here we employ mobile EEG to characterise the allocation of attention across multiple sensory-cognitive processing demands during naturalistic movement. We used a neural marker of attention, the Event-Related Potential (ERP) P300 effect, to show that attention to targets is reduced when human participants walk compared to when they stand still. In a second experiment, we show that this reduction in attention is not caused by the act of walking per se. A third experiment identified the independent processing demands driving reduced attention to target stimuli during motion. ERP data reveals that the reduction in attention seen during walking reflects the linear and additive sum of the processing demands produced by visual and inertial stimulation. The mobile cognition approach used here shows how limited resources are precisely re-allocated according to the sensory processing demands that occur during real-world behaviour.

Evaluating Mental Load During Realistic Driving Simulations by Means of Round the Ear Electrodes

Film based round the ear electrodes (cEEGrids) provide both, the accessibility of unobtrusive mobile EEG as well as a rapid EEG application in stationary settings when extended measurements are not possible. In a large-scale evaluation of driving abilities of older adults (N > 350) in a realistic driving simulation, we evaluated to what extent mental demands can be measured using cEEGrids in a completely unrestricted environment. For a first frequency-based analysis, the driving scenario was subdivided into different street segments with respect to their task loads (low, medium, high) that was a priori rated by an expert. Theta activity increased with task load but no change in Alpha power was found. Effects gained clarity after removing pink noise effects, that were potentially high in this data set due to motion artifacts. Theta fraction increased with task load and Alpha fraction decreased. We mapped this effect to specific street segments by applying a track-frequency analysis. Whilst participants drove with constant speed and without high steering wheel activity, Alpha was high and theta low. The reverse was the case in sections that required either high activity or increased attentional allocation to the driving context. When calculating mental demands for different street segments based on EEG, this measure is highly significant correlated with the experts' rating of task load. Deviances can be explained by specific features within the segments. Thus, modulations in spectral power of the EEG were validly reflected in the cEEGrids data. All findings were in line with the prominent literature in the field. The results clearly demonstrate the usability of this low-density EEG method for application in real-world settings where an increase in ecological validity might outweigh the loss of certain aspects of internal validity.

Effects of Different Re-referencing Methods on Spontaneously Generated Ear-EEG

In recent years, electroencephalography (EEG) measured around the ears, called ear-EEG, has been introduced to develop unobtrusive and ambulatory EEG-based applications. When measuring ear-EEGs, the availability of a reference site is restricted due to the miniaturized device structure, and therefore a reference electrode is generally placed near the recording electrodes. As the electrical brain activity recorded at a reference electrode closely placed to recording electrodes may significantly cancel or influence the brain activity recorded by the recording electrodes, an appropriate re-referencing method is often required to mitigate the impact of the reference brain activity. In this study, therefore, we systematically investigated the impact of different re-referencing methods on ear-EEGs spontaneously generated from endogenous paradigms. To this end, we used two ear-EEG datasets recorded behind both ears while subjects performed an alpha modulation task [eyes-closed (EC) and eyes-open (EO)] and two mental tasks [mental arithmetic (MA) and mental singing (MS)]. The measured ear-EEGs were independently re-referenced using five different methods: (i) all-mean, (ii) contralateral-mean, (iii) ipsilateral-mean, (iv) contralateral-bipolar, and (v) ipsilateral-bipolar. We investigated the changes in alpha power during EO and EC tasks, as well as event-related (de) synchronization (ERD/ERS) during MA and MS. To evaluate the effects of re-referencing methods on ear-EEGs, we estimated the signal-to-noise ratios (SNRs) of the two ear-EEG datasets, and assessed the classification performance of the two mental tasks (MA vs. MS). Overall patterns of changes in alpha power and ERD/ERS were similar among the five re-referencing methods, but the contralateral-mean method showed statistically higher SNRs than did the other methods for both ear-EEG datasets, except in the contralateral-bipolar method for the two mental tasks. In concordance with the SNR results, classification performance was also statistically higher for the contralateral-mean method than it was for the other re-referencing methods. The results suggest that employing contralateral mean information can be an efficient way to re-reference spontaneously generated ear-EEGs, thereby maximizing the reliability of ear-EEG-based applications in endogenous paradigms.

Acquisition of Subcortical Auditory Potentials With Around-the-Ear cEEGrid Technology in Normal and Hearing Impaired Listeners

Even though the principles of recording brain electrical activity remain unchanged since their discovery, their acquisition has seen major improvements. The cEEGrid, a recently developed flex-printed multi-channel sensory array, can be placed around the ear and successfully record well-known cortical electrophysiological potentials such as late auditory evoked potentials (AEPs) or the P300. Due to its fast and easy application as well as its long-lasting signal recording window, the cEEGrid technology offers great potential as a flexible and ‘wearable’ solution for the acquisition of neural correlates of hearing. Early potentials of auditory processing such as the auditory brainstem response (ABR) are already used in clinical assessment of sensorineural hearing disorders and envelope following responses (EFR) have shown promising results in the diagnosis of suprathreshold hearing deficits. This study evaluates the suitability of the cEEGrid electrode configuration to capture these AEPs. cEEGrid potentials were recorded and compared to cap-EEG potentials for young normal-hearing listeners and older listeners with high-frequency sloping audiograms to assess whether the recordings are adequately sensitive for hearing diagnostics. ABRs were elicited by presenting clicks (70 and 100-dB peSPL) and stimulation for the EFRs consisted of 120 Hz amplitude-modulated white noise carriers presented at 70-dB SPL. Data from nine bipolar cEEGrid channels and one classical cap-EEG montage (earlobes to vertex) were analysed and outcome measures were compared. Results show that the cEEGrid is able to record ABRs and EFRs with comparable shape to those recorded using a conventional cap-EEG recording montage and the same amplifier. Signal strength is lower but can still produce responses above the individual neural electrophysiological noise floor. This study shows that the application of the cEEGrid can be extended to the acquisition of early auditory evoked potentials.

Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy

Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here, we investigate automated sleep scoring based on a low-cost, mobile electroencephalogram (EEG) platform consisting of a lightweight EEG amplifier combined with flex-printed cEEGrid electrodes placed around the ear, which can be implemented as a fully self-applicable sleep system. However, cEEGrid signals have different amplitude characteristics to normal scalp PSG signals, which might be challenging for visual scoring. Therefore, this study evaluates the potential of automatic scoring of cEEGrid signals using a machine learning classifier ("random forests") and compares its performance with manual scoring of standard PSG. In addition, the automatic scoring of cEEGrid signals is compared with manual annotation of the cEEGrid recording and with simultaneous actigraphy. Acceptable recordings were obtained in 15 healthy volunteers (aged 35 ± 14.3 years) during an extended nocturnal sleep opportunity, which induced disrupted sleep with a large inter-individual variation in sleep parameters. The results demonstrate that machine-learning-based scoring of around-the-ear EEG outperforms actigraphy with respect to sleep onset and total sleep time assessments. The automated scoring outperforms human scoring of cEEGrid by standard criteria. The accuracy of machine-learning-based automated scoring of cEEGrid sleep recordings compared with manual scoring of standard PSG was satisfactory. The findings show that cEEGrid recordings combined with machine-learning-based scoring holds promise for large-scale sleep studies.

EEG-Based Brain-Computer Interfaces

Brain-computer interfaces (BCIs) provide a direct communication channel between human brain and output devices. Due to advantages such as non-invasiveness, ease of use, and low cost, electroencephalography (EEG) is the most popular method for current BCIs. This chapter gives an overview of the current EEG-based BCIs for the main purpose of communication and control. This chapter first provides a taxonomy of the EEG-based BCI systems by categorizing them into three major groups: (1) BCIs based on event-related potentials (ERPs), (2) BCIs based on sensorimotor rhythms, and (3) hybrid BCIs. Next, this chapter describes challenges and potential solutions in developing practical BCI systems toward high communication speed, convenient system use, and low user variation. Then this chapter briefly reviews both medical and non-medical applications of current BCIs. Finally, this chapter concludes with a summary of current stage and future perspectives of the EEG-based BCI technology.

Sleep EEG Derived From Behind-the-Ear Electrodes (cEEGrid) Compared to Standard Polysomnography: A Proof of Concept Study

Electroencephalography (EEG) recordings represent a vital component of the assessment of sleep physiology, but the methodology presently used is costly, intrusive to participants, and laborious in application. There is a recognized need to develop more easily applicable yet reliable EEG systems that allow unobtrusive long-term recording of sleep-wake EEG ideally away from the laboratory setting. cEEGrid is a recently developed flex-printed around-the-ear electrode array, which holds great potential for sleep-wake monitoring research. It is comfortable to wear, simple to apply, and minimally intrusive during sleep. Moreover, it can be combined with a smartphone-controlled miniaturized amplifier and is fully portable. Evaluation of cEEGrid as a motion-tolerant device is ongoing, but initial findings clearly indicate that it is very well suited for cognitive research. The present study aimed to explore the suitability of cEEGrid for sleep research, by testing whether cEEGrid data affords the signal quality and characteristics necessary for sleep stage scoring. In an accredited sleep laboratory, sleep data from cEEGrid and a standard PSG system were acquired simultaneously. Twenty participants were recorded for one extended nocturnal sleep opportunity. Fifteen data sets were scored manually. Sleep parameters relating to sleep maintenance and sleep architecture were then extracted and statistically assessed for signal quality and concordance. The findings suggest that the cEEGrid system is a viable and robust recording tool to capture sleep and wake EEG. Further research is needed to fully determine the suitability of cEEGrid for basic and applied research as well as sleep medicine.

On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main experiments where EEGs were measured on the scalp and behind the ears to check the reliability of ear-EEGs as compared to scalp-EEGs. In the preliminary and main experiments, subjects performed eyes-open and eyes-closed tasks, and they performed mental arithmetic (MA) and light cognitive (LC) tasks, respectively. For data analysis, the brain area was divided into four regions of interest (ROIs) (i.e., frontal, central, occipital, and ear area). The preliminary experiment showed that the degree of alpha activity increase of the ear area with eyes closed is comparable to those of other ROIs (occipital > ear > central > frontal). In the main experiment, similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs during MA and LC, and all ROIs showed the mean classification accuracies above 70% required for effective binary communication (MA vs. LC) (occipital = ear = central = frontal). From the results, we demonstrated that ear-EEG can be used to develop an endogenous BCI system based on cognitive tasks without external stimuli, which allows the usability of ear-EEGs to be extended.