Search Results (33)

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. Full article

Construction activities are often conducted in outdoor and harsh environments and involve long working hours and physical and mental labor, which can lead to significant mental fatigue among workers. This study introduces a novel and non-invasive method for monitoring and assessing mental fatigue in construction workers. Based on cognitive neuroscience theory, we analyzed the neurophysiological mapping of spontaneous mental fatigue and developed multimodal in-ear sensors specifically designed for construction workers. These sensors enable real-time and continuous integration of neurophysiological signals. A cognitive experiment was conducted to validate the proposed mental fatigue assessment method. Results demonstrated that all selected supervised classification models can accurately identify mental fatigue by using the recorded neurophysiological data, with evaluation metrics exceeding 80%. The long short-term memory model achieved an average accuracy of 92.437%. This study offers a theoretical framework and a practical approach for assessing the mental fatigue of on-site workers and provides a basis for the proactive management of occupational health and safety on construction sites. Full article

Tinnitus is the perception of sounds like ringing or buzzing in the ears without any external source, varying in intensity and potentially becoming chronic. This study aims to enhance the understanding and treatment of tinnitus by analyzing a dataset related to tinnitus therapy, focusing on electroencephalography (EEG) signals from patients undergoing treatment. The objectives of the study include applying various preprocessing techniques to ensure data quality, such as noise elimination and standardization of sampling rates, and extracting essential features from EEG signals, including power spectral density and statistical measures. The novelty of this research lies in its innovative approach to representing different channels of EEG signals as new graph network representations without losing any information. This transformation allows for the use of Graph Neural Networks (GNNs), specifically Graph Convolutional Networks (GCNs) combined with Long Short-Term Memory (LSTM) networks, to model intricate relationships and temporal dependencies within the EEG data. This method enables a comprehensive analysis of the complex interactions between EEG channels. The study reports an impressive accuracy rate of 99.41%, demonstrating the potential of this novel approach. By integrating graph representation and deep learning, this research introduces a new methodology for analyzing tinnitus therapy data, aiming to contribute to more effective treatment strategies for tinnitus sufferers. Full article

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for advancing brain monitoring technologies into everyday applications. However, despite the current availability of several in-ear EEG devices in the market, there remains a critical need for robust validation against established clinical-grade systems. In this study, we carried out a detailed examination of the signal performance of a mobile in-ear EEG device from Naox Technologies. Our investigation had two main goals: firstly, evaluating the hardware circuit’s reliability through simulated EEG signal experiments and, secondly, conducting a thorough comparison between the in-ear EEG device and gold-standard EEG monitoring equipment. This comparison assesses correlation coefficients with recognized physiological patterns during wakefulness and sleep, including alpha rhythms, eye artifacts, slow waves, spindles, and sleep stages. Our findings support the feasibility of using this in-ear EEG device for brain activity monitoring, particularly in scenarios requiring enhanced comfort and user-friendliness in various clinical and research settings. Full article

Digital health tracking is a source of valuable insights for public health research and consumer health technology. The brain is the most complex organ, containing information about psychophysical and physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red spectroscopy (fNIRS), and photoplethysmography (PPG) technologies have allowed the development of devices that can remotely monitor changes in brain activity. The inclusion criteria for the papers in this review encompassed studies on self-applied, remote, non-invasive neuroimaging techniques (EEG, fNIRS, or PPG) within healthcare applications. A total of 23 papers were reviewed, comprising 17 on using EEGs for remote monitoring and 6 on neurofeedback interventions, while no papers were found related to fNIRS and PPG. This review reveals that previous studies have leveraged mobile EEG devices for remote monitoring across the mental health, neurological, and sleep domains, as well as for delivering neurofeedback interventions. With headsets and ear-EEG devices being the most common, studies found mobile devices feasible for implementation in study protocols while providing reliable signal quality. Moderate to substantial agreement overall between remote and clinical-grade EEGs was found using statistical tests. The results highlight the promise of portable brain-imaging devices with regard to continuously evaluating patients in natural settings, though further validation and usability enhancements are needed as this technology develops. Full article

There is a need for seizure classification based on EEG signals that can be implemented with a portable device for in-home continuous minoring of epilepsy. In this study, we developed a novel machine learning algorithm for seizure detection suitable for wearable systems. Extreme gradient boosting (XGBoost) was implemented to classify seizures from single-channel EEG obtained from an open-source CHB-MIT database. The results of classifying 1-s EEG segments are shown to be sufficient to obtain the information needed for seizure detection and achieve a high seizure sensitivity of up to 89% with low computational cost. This algorithm can be impeded in single-channel EEG systems that use in- or around-the-ear electrodes for continuous seizure monitoring at home. Full article

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. Full article

In-ear acquisition of physiological signals, such as electromyography (EMG), electrooculography (EOG), electroencephalography (EEG), and electrocardiography (ECG), is a promising approach to mobile health (mHealth) due to its non-invasive and user-friendly nature. By providing a convenient and comfortable means of physiological signal monitoring, in-ear signal acquisition could potentially increase patient compliance and engagement with mHealth applications. The development of reliable and comfortable soft dry in-ear electrode systems could, therefore, have significant implications for both mHealth and human–machine interface (HMI) applications. This research evaluates the quality of the ECG signal obtained with soft dry electrodes inserted in the ear canal. An earplug with six soft dry electrodes distributed around its perimeter was designed for this study, allowing for the analysis of the signal coming from each electrode independently with respect to a common reference placed at different positions on the body of the participants. An analysis of the signals in comparison with a reference signal measured on the upper right chest (RA) and lower left chest (LL) was performed. The results show three typical behaviors for the in-ear electrodes. Some electrodes have a high correlation with the reference signal directly after inserting the earplug, other electrodes need a settling time of typically 1–3 min, and finally, others never have a high correlation. The SoftPulse^TM electrodes used in this research have been proven to be perfectly capable of measuring physiological signals, paving the way for their use in mHealth or HMI applications. The use of multiple electrodes distributed in the ear canal has the advantage of allowing a more reliable acquisition by intelligently selecting the signal acquisition locations or allowing a better spatial resolution for certain applications by processing these signals independently. Full article

Hearing loss is a prevalent health issue that affects individuals worldwide. Binaural hearing refers to the ability to integrate information received simultaneously from both ears, allowing individuals to identify, locate, and separate sound sources. Auditory evoked potentials (AEPs) refer to the electrical responses that are generated within any part of the auditory system in response to auditory stimuli presented externally. Electroencephalography (EEG) is a non-invasive technology used for the monitoring of AEPs. This research aims to investigate the use of audiometric EEGs as an objective method to detect specific features of binaural hearing with frequency and time domain analysis techniques. Thirty-five subjects with normal hearing and a mean age of 27.35 participated in the research. The stimuli used in the current study were designed to investigate the impact of binaural phase shifts of the auditory stimuli in the presence of noise. The frequency domain and time domain analyses provided statistically significant and promising novel findings. The study utilized Blackman windowed 18 ms and 48 ms pure tones as stimuli, embedded in noise maskers, of frequencies 125 Hz, 250 Hz, 500 Hz, 750 Hz, 1000 Hz in homophasic (the same phase in both ears) and antiphasic (180-degree phase difference between the two ears) conditions. The study focuses on the effect of phase reversal of auditory stimuli in noise of the middle latency response (MLR) and late latency response (LLR) regions of the AEPs. The frequency domain analysis revealed a significant difference in the frequency bands of 20 to 25 Hz and 25 to 30 Hz when elicited by antiphasic and homophasic stimuli of 500 Hz for MLRs and 500 Hz and 250 Hz for LLRs. The time domain analysis identified the Na peak of the MLR for 500 Hz, the N1 peak of the LLR for 500 Hz stimuli and the P300 peak of the LLR for 250 Hz as significant potential markers in detecting binaural processing in the brain. Full article

Wearable electroencephalography (EEG) has the potential to improve everyday life through brain–computer interfaces (BCI) for applications such as sleep improvement, adaptive hearing aids, or thought-based digital device control. To make these innovations more practical for everyday use, researchers are looking to miniaturized, concealed EEG systems that can still collect neural activity precisely. For example, researchers are using flexible EEG electrode arrays that can be attached around the ear (cEEGrids) to study neural activations in everyday life situations. However, the use of such concealed EEG approaches is limited by measurement challenges such as reduced signal amplitudes and high recording system costs. In this article, we compare the performance of a lower-cost open-source amplification system, the OpenBCI Cyton+Daisy boards, with a benchmark amplifier, the MBrainTrain Smarting Mobi. Our results show that the OpenBCI system is a viable alternative for concealed EEG research, with highly similar noise performance, but slightly lower timing precision. This system can be a great option for researchers with a smaller budget and can, therefore, contribute significantly to advancing concealed EEG research. Full article

Long-term home monitoring of people living with epilepsy cannot be achieved using the standard full-scalp electroencephalography (EEG) coupled with video. Wearable seizure detection devices, such as behind-the-ear EEG (bte-EEG), offer an unobtrusive method for ambulatory follow-up of this population. Combining bte-EEG with electrocardiography (ECG) can enhance automated seizure detection performance. However, such frameworks produce high false alarm rates, making visual review necessary. This study aimed to evaluate a semi-automated multimodal wearable seizure detection framework using bte-EEG and ECG. Using the SeizeIT1 dataset of 42 patients with focal epilepsy, an automated multimodal seizure detection algorithm was used to produce seizure alarms. Two reviewers evaluated the algorithm’s detections twice: (1) using only bte-EEG data and (2) using bte-EEG, ECG, and heart rate signals. The readers achieved a mean sensitivity of 59.1% in the bte-EEG visual experiment, with a false detection rate of 6.5 false detections per day. Adding ECG resulted in a higher mean sensitivity (62.2%) and a largely reduced false detection rate (mean of 2.4 false detections per day), as well as an increased inter-rater agreement. The multimodal framework allows for efficient review time, making it beneficial for both clinicians and patients. Full article

To expand the applications of the electroencephalogram (EEG), long-term measurement, a short installation time, and little stress on the participants are needed. In this study, we designed, fabricated, and evaluated an EEG headset with three candle-like microneedle electrodes (CMEs). The user is able to detach and reattach the electrodes, enabling long-term measurement with little stress. The design of the CMEs was experimentally determined by considering the skin-to-electrode impedance and user comfort. An EEG was successfully measured from areas with a high hair density without any preparation. The installation time was shorter than 60 s and the electrodes could be detached and reattached. The headset was designed such that the discomfort caused by its ear pads was higher than that caused by the electrodes. In 1 h experiments, the participants did not feel pain and the detachment of the CMEs was found to improve the comfort level of the participants in most cases. A successful demonstration of the long-term measurement of EEGs while watching a whole movie verified that the developed EEG headset with CMEs is applicable for EEG measurement in a variety of applications. Full article

Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients’ health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people. Full article

Transcutaneous auricular vagus nerve stimulation (taVNS) is a newer delivery system using a non-invasive stimulation device placed at the ear. taVNS research is focused on clinical trials showing potential therapeutic benefits, however the neurophysiological effects of this stimulation on brain activity are still unclear. We propose a systematic review that aims to describe the effects of taVNS on EEG measures and identify taVNS parameters that can potentially lead to consistent EEG-mediated biomarkers for this therapy. A systematic literature review was carried out following the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) and the Cochrane handbook for systematic reviews. Clinical trials examining EEG parameters were considered, including absolute and relative power, coherence, degree of symmetry, evoked potentials, and peak frequency of all bands. According to our criteria, 18 studies (from 122 articles) were included. Our findings show a general trend towards increased EEG power spectrum activity in lower frequencies, and changes on early components of the ERP related to inhibitory tasks. This review suggests that quantitative electroencephalography can be used to assess the effects of taVNS on brain activity, however more studies are needed to systematically establish the specific effects and metrics that would reflect the non-invasive stimulation through the auricular branch of the vagus nerve. Full article

Auditory steady-state response (ASSR) is a translational biomarker for several neurological and psychiatric disorders, such as hearing loss, schizophrenia, bipolar disorder, autism, etc. The ASSR is sinusoidal electroencephalography (EEG)/magnetoencephalography (MEG) responses induced by periodically presented auditory stimuli. Traditional frequency analysis assumes ASSR is a stationary response, which can be analyzed using linear analysis approaches, such as Fourier analysis or Wavelet. However, recent studies have reported that the human steady-state responses are dynamic and can be modulated by the subject’s attention, wakefulness state, mental load, and mental fatigue. The amplitude modulations on the measured oscillatory responses can result in the spectral broadening or frequency splitting on the Fourier spectrum, owing to the trigonometric product-to-sum formula. Accordingly, in this study, we analyzed the human ASSR by the combination of canonical correlation analysis (CCA) and Holo-Hilbert spectral analysis (HHSA). The CCA was used to extract ASSR-related signal features, and the HHSA was used to decompose the extracted ASSR responses into amplitude modulation (AM) components and frequency modulation (FM) components, in which the FM frequency represents the fast-changing intra-mode frequency and the AM frequency represents the slow-changing inter-mode frequency. In this paper, we aimed to study the AM and FM spectra of ASSR responses in a 37 Hz steady-state auditory stimulation. Twenty-five healthy subjects were recruited for this study, and each subject was requested to participate in two auditory stimulation sessions, including one right-ear and one left-ear monaural steady-state auditory stimulation. With the HHSA, both the 37 Hz (fundamental frequency) and the 74 Hz (first harmonic frequency) auditory responses were successfully extracted. Examining the AM spectra, the 37 Hz and the 74 Hz auditory responses were modulated by distinct AM spectra, each with at least three composite frequencies. In contrast to the results of traditional Fourier spectra, frequency splitting was seen at 37 Hz, and a spectral peak was obscured at 74 Hz in Fourier spectra. The proposed method effectively corrects the frequency splitting problem resulting from time-varying amplitude changes. Our results have validated the HHSA as a useful tool for steady-state response (SSR) studies so that the misleading or wrong interpretation caused by amplitude modulation in the traditional Fourier spectrum can be avoided. Full article