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Ear-EEG: Views of earplug for the right ear. (a) Right ear earplug marked with labels. (b) Earplug, electrodes ERA, ERB, and ERH are visible. (c) Opposite view to (b). Connector and electrode ERE are visible. (d) Earplug with the connector attached. (e) Right ear with the earplug in place. (f) Right side view of the measurement setup.
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A method for brain monitoring based on measuring the electroencephalograph (EEG) from electrodes placed in-theear (ear-EEG) was recently proposed. The objective of this work is to further characterize the ear-EEG and perform a rigorous comparison against conventional on-scalp EEG. This is achieved for both auditory and visual evoked responses, over...
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... propagate unimpeded to the eardrum. On each earpiece are placed four electrodes, and for all the recordings reported in this paper the electrode positions were ExA, ExB, ExE, and ExH for both x=L and x=R (see the labelling scheme in Appendix A). The electrode areas were approximately 20 mm 2 ; an example of an earpiece with electrodes is shown in Fig. 3(b) and ...
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... ExH. A distinguishing feature of our ear-EEG system is that the recordings are truly in-the-ear measurements; that is, all the electrodes, including the reference and ground electrodes, are placed within the ear, and are galvanically in- sulated from any of the electrode on the subject (e.g., scalp electrodes and electrodes in the opposite ear). Fig. 3(e) and (f) shows the experimental setup used, that allows for a simultane- ous recording of ear-EEG and standard on-scalp ...
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... concha part of the ear; the letter D denotes the ear lobe electrode, and the letters E through L denote electrode positions in the ear canal. The labelling scheme is illustrated in Fig. 11 for the left ear, the same labelling scheme applies to the right ear. For instance, electrode ERB is an electrode placed in the concha region of the right ear. Fig. 3(a) shows a photo of a blank earpiece with the electrode positions indicated. The elec- trodes in the ear canal are located before the bony part of the ear canal (see also Fig. 2), and the electrode position is defined by the direction (angle) of the electrode relative to the vertical axis. The vertical axis is defined as perpendicular to ...
Citations
... An extensive study recently confirmed that the auditory N100, MMN, P300 and N400 could be recorded reliably with electrodes placed around the ears (Meiser and Bleichner, 2022), despite some expected signal loss as compared to standard scalp-EEG positions. In fact, specialized ear-EEG systems already exist: Examples include tiny EEG sensors that are placed into the concha and outer ear canal (Looney et al., 2011;Kidmose et al., 2013) as well as devices that are positioned around or behind the ear (Do Valle et al., 2014), particularly the cEEGrid (Debener et al., 2015). The cEEGrid is a semi-disposable, flex-printed sensor array of ten Ag/AgCl electrodes per ear ( Figure 1 shows the channel configuration for the right ear). ...
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.
... ASSR recordings are typically performed in the clinic and require dedicated equipment and trained personnel. 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A c c e p t e d M a n u s c r i p t Over the last decade, a new EEG recording approach called ear-EEG has been developed [8,9]. Here, EEG electrodes are placed in or around the ear, allowing the recording platform to be more discreet. ...
... The 12 ear electrodes, six in each ear, were placed on individually designed earpieces in positions according to the labeling scheme for ear-EEG electrodes described by Kidmose et al. [8]. For the current study, the following earelectrode positions were used: ExA, ExB1, ExB2 and ExC in the concha part of the ear, ExJ in the ear-canal, and ExT A c c e p t e d M a n u s c r i p t located on the tragus, where x denotes the left (L) or right (R) ear (see Figure 1). ...
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 minutes 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.
... Second, while the participants practiced until they reached a plateau for performance in MATB prior to the collection of EEG data, it is impossible to rule out if any additional learning occurred over the period of five data collection days, which may impact the BRO responses reported here. Furthermore, while the current study utilized data from a whole-head 19-channel EEG system, future works should evaluate the findings using lower density and alternate location sensors [86][87][88] that enable easier translation into real-world naturalistic settings (e.g., actual aircraft cockpits [89]) when paired with appropriate signal processing techniques [48,[90][91][92]. ...
Background: There is a significant need to monitor human cognitive performance in complex environments, with one example being pilot performance. However, existing assessments largely focus on subjective experiences (e.g., questionnaires) and the evaluation of behavior (e.g., aircraft handling) as surrogates for cognition or utilize brainwave measures which require artificial setups (e.g., simultaneous auditory stimuli) that intrude on the primary tasks. Blink-related oscillations (BROs) are a recently discovered neural phenomenon associated with spontaneous blinking that can be captured without artificial setups and are also modulated by cognitive loading and the external sensory environment—making them ideal for brain function assessment within complex operational settings. Methods: Electroencephalography (EEG) data were recorded from eight adult participants (five F, M = 21.1 years) while they completed the Multi-Attribute Task Battery under three different cognitive loading conditions. BRO responses in time and frequency domains were derived from the EEG data, and comparisons of BRO responses across cognitive loading conditions were undertaken. Simultaneously, assessments of blink behavior were also undertaken. Results: Blink behavior assessments revealed decreasing blink rate with increasing cognitive load (p < 0.001). Prototypical BRO responses were successfully captured in all participants (p < 0.001). BRO responses reflected differences in task-induced cognitive loading in both time and frequency domains (p < 0.05). Additionally, reduced pre-blink theta band desynchronization with increasing cognitive load was also observed (p < 0.05). Conclusion: This study confirms the ability of BRO responses to capture cognitive loading effects as well as preparatory pre-blink cognitive processes in anticipation of the upcoming blink during a complex multitasking situation. These successful results suggest that blink-related neural processing could be a potential avenue for cognitive state evaluation in operational settings—both specialized environments such as cockpits, space exploration, military units, etc. and everyday situations such as driving, athletics, human-machine interactions, etc.—where human cognition needs to be seamlessly monitored and optimized.
... They also enable easier and less intrusive data collection, making monitoring in non-clinical environments feasible. In this regard, studies comparing EEG signals from around the ears and scalp EEG indicate they can reliably capture brain activity [58][59][60][61][62]. However, despite these advantages, challenges such as bulkiness, discomfort, and high false detection rates persist in current wearable devices. ...
Epilepsy is one of the most common neurological diseases globally (around 50 million people worldwide). Fortunately, up to 70% of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The scalp-based EEG test, despite being the gold standard for diagnosing epilepsy, is costly, necessitates hospitalization, demands skilled professionals for operation, and is discomforting for users. In this paper, we propose EarSD, a novel lightweight, unobtrusive, and socially acceptable ear-worn system to detect epileptic seizure onsets by measuring the physiological signals from behind the user's ears. EarSD includes an integrated custom-built sensing-computing-communication PCB to collect and amplify the signals of interest, remove the noises caused by motion artifacts and environmental impacts, and stream the data wirelessly to the computer/mobile phone nearby, where data are uploaded to the host computer for further processing. We conducted both in-lab and in-hospital experiments with epileptic seizure patients who were hospitalized for seizure studies. The preliminary results confirm that EarSD can detect seizures with up to 95.3% accuracy by just using classical machine learning algorithms.
... ITE electrode showed excellent correlation and coherence with onscalp electrodes and was proven to extract several key EEG features, including the auditory steady state response (ASSR), alpha attenuation response (AAR), and P300 paradigms, which illustrated the potential of earEEG in BCI application [83,84]. A further study demonstrated that the signaltonoise ratio of the earEEG signal was comparable to that of the EEG recorded in the temporal region [85]. ...
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.
... The idea behind the ear-EEG technology originates from the requirement for a discreet, unobtrusive, robust, user-friendly, and feasible EEG system for sleep monitoring [66]. The ear-EEG signal is captured through the integration of electrodes within a specialized earpiece. ...
... The electrode composition, amplification mechanisms, and underlying principles mirror those utilized in on-scalp EEG recordings. However, these systems have a reduced number of electrodes compared to the conventional EEG systems, but their efficacy in delivering high-quality EEG signals has been proven, especially in brain-computer interface applications [66][67][68]. Moreover, this technology has been recently used to monitor various physiological responses beyond EEG, including cardiac activity [69,70]. ...
... The ear-EEG wearable system is designed for long-term comfort, and its electrodes are securely placed inside the ear canal to ensure recordings of high-quality signals. Despite the low signal amplitude in comparison to scalp EEG, the signal-to-noise ratio (SNR) was found to be similar, highlighting its reliability [45,66,67]. ...
Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.
... Following this, Prof. Vidal of California University, developed BCI to achieve a technology to read the signals of the brain [11]. It wasn't until 1977 that Aranibar and Pfurtscheller conducted experiments to demonstrate that moving or envisioning moving specific body parts could cause changes in the frequency spectrum of EEG alpha (8)(9)(10)(11)(12) and beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25) signals in the motor brain region. These changes would take place both at the start of the movement and throughout the process of migration. ...
... For patients with complicated eye impairment developed [16]. In order to make the previously used ASSR stimuli more logical and natural given in [17]. Jiang et al. created ERP-NF-BCI platformbased novel BCI systems, that use neurofeedback for communicating with the brain during training. ...
... In the case of EEG analysis, the DFT can be used to identify specific frequency bands that are related to various mental conditions and processes. For example, alpha waves with a frequency band (8)(9)(10)(11)(12) are associated with relaxation and meditation, while beta waves with a frequency band (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) are associated with alertness and cognitive processing. Overall, the use of digital signal processing techniques like the DFT has revolutionized the field of EEG analysis and has led to significant advances in our understanding of the brain and its function. ...
The BCI (Brain-computer interface) is a new-age tool in which human or artificial body parts like prosthetic arms can be controlled by sensing the EEG signals. To understand the mental state of the brain, the study of EEG is very important but this technique has its own limitation, which has been identified in this work. Various signal-processing techniques of EEG have been studied to analyze the mental state of the brain. A novel cross-technique will be introduced to improve EEG signal processing techniques. The spatial and temporal resolution trade-off problem is the biggest challenge in EEG signal acquisition systems. This is resolved by taking the fMRI signal and then transforming it into an EEG signal by decomposition and comparing modules for better spatial resolution in the EEG signal. The main limitations like frequency overlapping and decomposition, of the feature extraction technique, have been investigated and an improvised general algorithm will be introduced in this proposed work. This work also touches briefly on different classification algorithms which follow different learning criteria and optimized techniques will be used to achieve better performance. The main objective of this proposed work is the identification of the best-suited algorithm for each step of signal processing to understand the mental state of the brain, brain oscillation characteristics, and some old and new techniques trends companion for the analysis of signal processing techniques of the brain.
... Additionally, earlobe is much less sensitive than the scalp, which makes ear EEG devices less intrusive and more comfortable to use for patients, espeically for applications such as the detection and alert of FoG. To the best of our knowledge, FoG detection using ear EEG signal has not been explored, despite evidences suggest that ear EEG signals are strongly correlated to cap EEG signals [9]- [11]. This work's development of wearable sensors represents a significant advancement towards the validation of the clinical effectiveness of detecting FoG and other neurological disorders in future clinical trials. ...
Freezing of gait (FoG) is a debilitating symptom of Parkinson's disease (PD). This work develops flexible wearable sensors that can detect FoG and alert patients and companions to help prevent falls. FoG is detected on the sensors using a deep learning (DL) model with multi-modal sensory inputs collected from distributed wireless sensors. Two types of wireless sensors are developed, including: (1) a C-shape central node placed around the patient's ears, which collects electroencephalogram (EEG), detects FoG using an on-device DL model, and generates auditory alerts when FoG is detected; (2) a stretchable patch-type sensor attached to the patient's legs, which collects electromyography (EMG) and movement information from accelerometers. The patch-type sensors wirelessly send collected data to the central node through low-power ultra-wideband (UWB) transceivers. All sensors are fabricated on flexible printed circuit boards. Adhesive gel-free acetylene carbon black and polydimethylsiloxane electrodes are fabricated on the flexible substrate to allow conformal wear over the long term. Custom integrated circuits (IC) are developed in 180 nm CMOS technology and used in both types of sensors for signal acquisition, digitization, and wireless communication. A novel lightweight DL model is trained using multi-modal sensory data. The inference of the DL model is performed on a low-power microcontroller in the central node. The DL model achieves a high detection sensitivity of 0.81 and a specificity of 0.88. The developed wearable sensors are ready for clinical experiments and hold great promise in improving the quality of life of patients with PD. The proposed design methodologies can be used in wearable medical devices for the monitoring and treatment of a wide range of neurodegenerative diseases.
... The potential difference across the heart is often as much as 2 orders of magnitude lower from the ear than it is at the chest [7]. Moreover, the Ear-ECG commonly contains other signals comparable in amplitude, such as electrical activity generated by eye movements, known as electrooculography (EOG) [8], and electrical signals generated by neuronal activity in the brain, known as electroencephalography (EEG) [9][10] [11]. In order to best exploit the benefits of Ear-ECG, algorithms need to be able to detect the presence of ECG waveform across challenging range of signal qualities, and correctly distinguish the peaks in ECG (R-peaks) from peaks that may occur due to artefacts or other electrical activity. ...
The Ear-ECG provides a continuous Lead I electrocardiogram (ECG) by measuring the potential difference related to heart activity using electrodes that can be embedded within earphones. The significant increase in wearability and comfort afforded by Ear-ECG is often accompanied by a corresponding degradation in signal quality - a common obstacle that is shared by most wearable technologies. We aim to resolve this issue by introducing a Deep Matched Filter (Deep-MF) for the highly accurate detection of R-peaks in wearable ECG, thus enhancing the utility of Ear-ECG in real-world scenarios. The Deep-MF consists of an encoder stage (trained as part of an encoder-decoder module to reproduce ground truth ECG), and an R-peak classifier stage. Through its operation as a Matched Filter, the encoder searches for matches with an ECG template pattern in the input signal, prior to filtering the matches with the subsequent convolutional layers and selecting peaks corresponding to true ECG matches. The so condensed latent representation of R-peak information is then fed into a simple R-peak classifier, of which the output provides precise R-peak locations. The proposed Deep Matched Filter is evaluated using leave-one-subject-out cross validation over 36 subjects with an age range of 18-75, with the Deep-MF outperforming existing algorithms for R-peak detection in noisy ECG. The Deep-MF achieves a median R-peak recall of 94.9\%, a median precision of 91.2\% and an (AUC) value of 0.97. Furthermore, we demonstrate that the Deep Matched Filter algorithm not only retains the initialised ECG kernel structure during the training process, but also amplifies portions of the ECG which it deems most valuable. Overall, the Deep Matched Filter serves as a valuable step forward for the real-world functionality of Ear-ECG and, through its explainable operation, the acceptance of deep learning models in e-health.
... The prefrontal cortex is associated with sustained attention, emotions, working memory, and executive planning [16,47], Furthermore, recent evidence suggests that it may be an integral part for visual perception and recognition [48]. Additionally, prefrontal EEG channels have several attractive properties for real-world applications: discreet (not clearly visible), unobtrusive, comfortable to wear, impeding the user as little as possible, and user-friendly, since they can be operated and attached by the user [49,50]. However, there is a compromise in the recording quality resulting into noisy signals, with low SNR. ...
In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the Sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy).
