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Journal articles on the topic 'Wearable EEG'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Von Rosenberg, Wilhelm, Theerasak Chanwimalueang, Valentin Goverdovsky, David Looney, David Sharp, and Danilo P. Mandic. "Smart Helmet: Wearable Multichannel ECG and EEG." IEEE Journal of Translational Engineering in Health and Medicine 4 (2016): 1–11. http://dx.doi.org/10.1109/jtehm.2016.2609927.

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Kannan, Ramani, Syed Saad Azhar Ali, Abdulrehman Farah, Syed Hasan Adil, and Amjad Khan. "Smart Wearable EEG Sensor." Procedia Computer Science 105 (2017): 138–43. http://dx.doi.org/10.1016/j.procs.2017.01.193.

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3

Casson, Alexander J. "Wearable EEG and beyond." Biomedical Engineering Letters 9, no. 1 (2019): 53–71. http://dx.doi.org/10.1007/s13534-018-00093-6.

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Nishit Agarwal, Venkata Ramanaiah Chintha, Raja Kumar Kolli, Om Goel, and Raghav Agarwal. "Deep Learning for Real time EEG Artifact Detection in Wearables." International Journal for Research Publication and Seminar 13, no. 5 (2022): 402–33. http://dx.doi.org/10.36676/jrps.v13.i5.1510.

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Electroencephalography (EEG) has become a valuable tool for monitoring brain activity in both clinical and consumer applications. However, EEG signals collected from wearable devices are often disrupted by artifacts such as eye blinks, muscle movements, and external noise, which can severely compromise the accuracy of real-time analysis. Traditional methods for artifact detection and removal rely on manual techniques or simple filtering, making them unsuitable for continuous, real-time applications, particularly in mobile and wearable devices. This study explores the use of deep learning for r

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Naruse, Yasushi, and Yusuke Yokota. "EEG Measurement in Naturalistic Environments with Wearable EEG Device." Brain & Neural Networks 23, no. 3 (2016): 104–11. http://dx.doi.org/10.3902/jnns.23.104.

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Ahn, Joong Woo, Yunseo Ku, and Hee Chan Kim. "A Novel Wearable EEG and ECG Recording System for Stress Assessment." Sensors 19, no. 9 (2019): 1991. http://dx.doi.org/10.3390/s19091991.

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Suffering from continuous stress can lead to serious psychological and even physical disorders. Objective stress assessment methods using noninvasive physiological responses such as heart rate variability (HRV) and electroencephalograms (EEG) have therefore been proposed for effective stress management. In this study, a novel wearable device that can measure electrocardiograms (ECG) and EEG simultaneously was designed to enable continuous stress monitoring in daily life. The developed system is easily worn by hanging from both ears, is lightweight (i.e., 42.5 g), and exhibits an excellent nois

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Mandekar, Swati, Abigail Holland, Moritz Thielen, Mehdi Behbahani, and Mark Melnykowycz. "Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG." Sensors 22, no. 4 (2022): 1568. http://dx.doi.org/10.3390/s22041568.

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Wearable EEG has gained popularity in recent years driven by promising uses outside of clinics and research. The ubiquitous application of continuous EEG requires unobtrusive form-factors that are easily acceptable by the end-users. In this progression, wearable EEG systems have been moving from full scalp to forehead and recently to the ear. The aim of this study is to demonstrate that emerging ear-EEG provides similar impedance and signal properties as established forehead EEG. EEG data using eyes-open and closed alpha paradigm were acquired from ten healthy subjects using generic earpieces

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Zhang, Jingwei, Christos Chatzichristos, Kaat Vandecasteele, et al. "Automatic annotation correction for wearable EEG based epileptic seizure detection." Journal of Neural Engineering 19, no. 1 (2022): 016038. http://dx.doi.org/10.1088/1741-2552/ac54c1.

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Abstract Objective. Video-electroencephalography (vEEG), which defines the ground truth for the detection of epileptic seizures, is inadequate for long-term home monitoring. Thanks to advantages in comfort and unobtrusiveness, wearable EEG devices have been suggested as a solution for home monitoring. However, one of the challenges in data-driven automated seizure detection with wearable EEG data is to have reliable seizure annotations. Seizure annotations on the gold-standard 25-channel vEEG recordings may not be optimal to delineate seizure activity on the concomitantly recorded wearable EEG

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Daru, Richie Ranaisa, Monjur Morshed Rabby, Tina Ko, Yukti Shinglot, Rassel Raihan, and Ashfaq Adnan. "Electrically Equivalent Head Tissue Materials for Electroencephalogram Study on Head Surrogates." Applied Sciences 14, no. 6 (2024): 2495. http://dx.doi.org/10.3390/app14062495.

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With the recent advent of smart wearable sensors for monitoring brain activities in real-time, the scopes for using Electroencephalograms (EEGs) and Magnetoencephalography (MEG) in mobile and dynamic environments have become more relevant. However, their application in dynamic and open environments, typical of mobile wearable use, poses challenges. Presently, there is limited clinical data on using EEG/MEG as wearables. To advance these technologies at a time when large-scale clinical trials are not feasible, many researchers have turned to realistic phantom heads to further explore EEG and ME

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Uchitel, Julie, Ernesto E. Vidal-Rosas, Robert J. Cooper, and Hubin Zhao. "Wearable, Integrated EEG–fNIRS Technologies: A Review." Sensors 21, no. 18 (2021): 6106. http://dx.doi.org/10.3390/s21186106.

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There has been considerable interest in applying electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) simultaneously for multimodal assessment of brain function. EEG–fNIRS can provide a comprehensive picture of brain electrical and hemodynamic function and has been applied across various fields of brain science. The development of wearable, mechanically and electrically integrated EEG–fNIRS technology is a critical next step in the evolution of this field. A suitable system design could significantly increase the data/image quality, the wearability, patient/subject co

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Mansi, Silvia Angela, Ilaria Pigliautile, Camillo Porcaro, Anna Laura Pisello, and Marco Arnesano. "Application of wearable EEG sensors for indoor thermal comfort measurements." ACTA IMEKO 10, no. 4 (2021): 214. http://dx.doi.org/10.21014/acta_imeko.v10i4.1180.

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Multidomain comfort theories have been demonstrated to interpret human thermal comfort in buildings by employing human-centered physiological measurements coupled with environmental sensing techniques. Thermal comfort has been correlated with brain activity through electroencephalographic (EEG) measurements. However, the application of low-cost wearable EEG sensors for measuring thermal comfort has not been thoroughly investigated. Wearable EEG devices provide several advantages in terms of reduced intrusiveness and application in real-life contexts. However, they are prone to measurement unce

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Premchand, Brian, Liyuan Liang, Kok Soon Phua, et al. "Wearable EEG-Based Brain–Computer Interface for Stress Monitoring." NeuroSci 5, no. 4 (2024): 407–28. http://dx.doi.org/10.3390/neurosci5040031.

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Detecting stress is important for improving human health and potential, because moderate levels of stress may motivate people towards better performance at cognitive tasks, while chronic stress exposure causes impaired performance and health risks. We propose a Brain–Computer Interface (BCI) system to detect stress in the context of high-pressure work environments. The BCI system includes an electroencephalogram (EEG) headband with dry electrodes and an electrocardiogram (ECG) chest belt. We collected EEG and ECG data from 40 participants during two stressful cognitive tasks: the Cognitive Vig

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Mansi, Silvia Angela, Chiara Pappalettera, Fabrizio Vecchio, and Marco Arnesano. "A novel approach based on EEG Entropy measurement for indoor human thermal comfort estimation." E3S Web of Conferences 396 (2023): 01011. http://dx.doi.org/10.1051/e3sconf/202339601011.

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This paper presents a methodology for the application of electroencephalographic (EEG) Entropy measurements for indoor thermal comfort estimation. Wearables have been demonstrated to be capable of providing accurate physiological measurements to interpret individual thermal responses. Several studies demonstrated the correlation between the EEG Power Spectrum Density (PSD) variation and the subjects' responses exposed to different ambient temperatures. We present a complementary approach based on Approximate Entropy (ApEn) of EEG as a measure for the predictability of EEG series in describing

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Lunsford-Avery, Jessica R., Casey Keller, Scott H. Kollins, Andrew D. Krystal, Leah Jackson, and Matthew M. Engelhard. "Feasibility and Acceptability of Wearable Sleep Electroencephalogram Device Use in Adolescents: Observational Study." JMIR mHealth and uHealth 8, no. 10 (2020): e20590. http://dx.doi.org/10.2196/20590.

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Background Adolescence is an important life stage for the development of healthy behaviors, which have a long-lasting impact on health across the lifespan. Sleep undergoes significant changes during adolescence and is linked to physical and psychiatric health; however, sleep is rarely assessed in routine health care settings. Wearable sleep electroencephalogram (EEG) devices may represent user-friendly methods for assessing sleep among adolescents, but no studies to date have examined the feasibility and acceptability of sleep EEG wearables in this age group. Objective The goal of the research

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Park, Seonghun, Chang-Hee Han, and Chang-Hwan Im. "Design of Wearable EEG Devices Specialized for Passive Brain–Computer Interface Applications." Sensors 20, no. 16 (2020): 4572. http://dx.doi.org/10.3390/s20164572.

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Owing to the increased public interest in passive brain–computer interface (pBCI) applications, many wearable devices for capturing electroencephalogram (EEG) signals in daily life have recently been released on the market. However, there exists no well-established criterion to determine the electrode configuration for such devices. Herein, an overall procedure is proposed to determine the optimal electrode configurations of wearable EEG devices that yield the optimal performance for intended pBCI applications. We utilized two EEG datasets recorded in different experiments designed to modulate

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Bhagubai, Miguel, Kaat Vandecasteele, Lauren Swinnen, et al. "The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG." Bioengineering 10, no. 4 (2023): 491. http://dx.doi.org/10.3390/bioengineering10040491.

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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

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Lin, Jiahao. "Wearable Emotion Recognition Technology Based on Heart Rate Variability and EEG Rhythm." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 892–98. http://dx.doi.org/10.62051/g4rwwf66.

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This paper explores the utilization of physiological signals, including heart rate variability (HRV) and electroencephalography (EEG), in emotion recognition through wearable devices. Heart Rate Variability (HRV) is closely linked to emotional arousal. HRV can detect subtle changes in heart rate patterns, which are indicative of different emotional states. By analyzing these patterns, researchers can identify and differentiate between various emotions someone may be experiencing. The integration of heart sound signals alongside traditional ECG signals presents an innovative approach, enhancing

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노병호. "Non-EEG Seizure Detection Using Wearable Device." Journal of the korean child neurology society 25, no. 4 (2017): 209–14. http://dx.doi.org/10.26815/jkcns.2017.25.4.209.

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Jiang, Yubing, Nguyen Trung Hau, and Wan-Young Chung. "Semiasynchronous BCI Using Wearable Two-Channel EEG." IEEE Transactions on Cognitive and Developmental Systems 10, no. 3 (2018): 681–86. http://dx.doi.org/10.1109/tcds.2017.2716973.

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Li, Xiaodong, Shuoheng Yang, Ningbo Fei, Junlin Wang, Wei Huang, and Yong Hu. "A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG." Bioengineering 11, no. 6 (2024): 613. http://dx.doi.org/10.3390/bioengineering11060613.

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The application of wearable electroencephalogram (EEG) devices is growing in brain–computer interfaces (BCI) owing to their good wearability and portability. Compared with conventional devices, wearable devices typically support fewer EEG channels. Devices with few-channel EEGs have been proven to be available for steady-state visual evoked potential (SSVEP)-based BCI. However, fewer-channel EEGs can cause the BCI performance to decrease. To address this issue, an attention-based complex spectrum–convolutional neural network (atten-CCNN) is proposed in this study, which combines a CNN with a s

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Rehman, Mahnoor, Lindsay M. Higdon, and Michael R. Sperling. "Long-Term Home EEG Recording: Wearable and Implantable Devices." Journal of Clinical Neurophysiology 41, no. 3 (2023): 200–206. http://dx.doi.org/10.1097/wnp.0000000000001014.

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Summary: Electroencephalography (EEG) monitoring has served as a cornerstone in the diagnostic and therapeutic evaluation of epilepsy since its development. This has been accomplished with short-term inpatient video-EEG hospitalization enabling observation of both the semiological and the electrographic features of seizures or with short-term home ambulatory EEG or video-EEG. The advantages of inpatient video-EEG monitoring are limited by high cost, inconvenience, and inability to monitor patients for long periods (weeks or months) as might be done in the outpatient setting. This limitation ha

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Carretero, Ana, and Alvaro Araujo. "Design Decisions for Wearable EEG to Detect Motor Imagery Movements." Sensors 24, no. 15 (2024): 4763. http://dx.doi.org/10.3390/s24154763.

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The objective of this study was to make informed decisions regarding the design of wearable electroencephalography (wearable EEG) for the detection of motor imagery movements based on testing the critical features for the development of wearable EEG. Three datasets were utilized to determine the optimal acquisition frequency. The brain zones implicated in motor imagery movement were analyzed, with the aim of improving wearable-EEG comfort and portability. Two detection algorithms with different configurations were implemented. The detection output was classified using a tool with various class

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Assim, Ola Marwan, and Ahlam Fhathl Mahmood. "Designing a Wearable EEG Device and Its Benefits for Epilepsy Patients: A Review." Al-Kitab Journal for Pure Sciences 7, no. 1 (2023): 69–82. http://dx.doi.org/10.32441/kjps.07.01.p7.

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Epilepsy is a neurological disorder that causes repeated seizures in millions of people worldwide. Traditional Electroencephalography (EEG) systems can be cumbersome and limited to clinical settings, but they have helped diagnose and monitor epilepsy. Wearable EEG devices have transformed epilepsy management by providing real-time, non-invasive, and continuous monitoring capabilities. This review paper investigates the design considerations and technological advancements in wearable EEG devices, emphasizing their numerous benefits in treating epileptic patients and the limitation of designing

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D, Rosy Salomi Victoria, Aparna K, Balaji K, and Sudeshna Seal. "INTEGRATED CIRCUITS AND DEVICES APPLICATION IN WEARABLE HEALTHCARE SYSTEMS." ICTACT Journal on Microelectronics 9, no. 2 (2023): 1551–56. https://doi.org/10.21917/ijme.2023.0270.

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In recent years, there has been a growing interest in the development of wearable healthcare systems, particularly in the field of electroencephalography (EEG) for brain activity monitoring. Integrated Internet of Things (IoT) EEG electrodes circuits and devices have shown promising potential for high-gain output and enhanced data accuracy in such wearable systems. This research presents the design and analysis of IoT-based EEG electrode circuits and devices, aiming to optimize their performance for efficient brain signal acquisition. The proposed EEG electrodes circuit utilizes advanced signa

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Bleichner, Martin G., and Stefan Debener. "Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG." Frontiers in Human Neuroscience 11 (June 5, 2017): 163. https://doi.org/10.3389/fnhum.2017.00163.

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Kim, Youn, Jin-Yong Hong, Young-Pyo Jeon, Jung Bin Park, Cheol Jin Lee, and Jea Uk Lee. "Fabrication of Wearable Transistor with All-Graphene Electrodes via Hot Pressing." Polymers 14, no. 13 (2022): 2602. http://dx.doi.org/10.3390/polym14132602.

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Textile electronics are ideal for novel electronic devices owing to their flexibility, light weight, and wearability. In this work, wearable organic field-effect transistors (OFETs) with all-graphene electrodes, fabricated using hot pressing, are described. First, highly conductive and flexible electrodes consisting of a cotton textile substrate and electrochemically exfoliated graphene (EEG) were prepared via hot pressing. The EEG/textile electrodes exhibited a low sheet resistance of 1.3 Ω sq−1 and high flexibility; these were used as gate electrodes in the wearable OFETs. In addition, spray

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Li, Boyu, Mingjie Li, Jie Xia, Hao Jin, Shurong Dong, and Jikui Luo. "Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring." Sensors 24, no. 15 (2024): 4847. http://dx.doi.org/10.3390/s24154847.

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Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need for an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG and deep brain fNIRS signals. Here, we developed a hybrid EEG-fNIRS patch capable of acquiring high-quality, co-located EEG and fNIRS signals. This patch is wearable and provides easy cognition and emotion detection, while reducing the spatial interference and signal crosstal

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Grosselin, Fanny, Xavier Navarro-Sune, Alessia Vozzi, et al. "Quality Assessment of Single-Channel EEG for Wearable Devices." Sensors 19, no. 3 (2019): 601. http://dx.doi.org/10.3390/s19030601.

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The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. The performance of our method were assessed on different dat

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Moontaha, Sidratul, Franziska Elisabeth Friederike Schumann, and Bert Arnrich. "Online Learning for Wearable EEG-Based Emotion Classification." Sensors 23, no. 5 (2023): 2387. http://dx.doi.org/10.3390/s23052387.

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Giving emotional intelligence to machines can facilitate the early detection and prediction of mental diseases and symptoms. Electroencephalography (EEG)-based emotion recognition is widely applied because it measures electrical correlates directly from the brain rather than indirect measurement of other physiological responses initiated by the brain. Therefore, we used non-invasive and portable EEG sensors to develop a real-time emotion classification pipeline. The pipeline trains different binary classifiers for Valence and Arousal dimensions from an incoming EEG data stream achieving a 23.9

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Sun, Wenbin, Zilong Guo, Zhiqiang Yang, et al. "A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors." Sensors 22, no. 20 (2022): 7784. http://dx.doi.org/10.3390/s22207784.

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In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor the

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Tseghai, G. B., B. Malengier, K. A. Fante, and L. Van Langenhove. "Loop Fabric EEG Textrode for Brain Activity Monitoring." IOP Conference Series: Materials Science and Engineering 1266, no. 1 (2023): 012019. http://dx.doi.org/10.1088/1757-899x/1266/1/012019.

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Abstract Recently, metallic dry EEG electrodes have been introduced to overcome the limitation of wet electrodes, as the conductive gel used causes skin irritation and dries out over time. However, the metal dry EEG electrodes have a high weight and a stiff structure that limits them from wearable application. In this work, we have developed a textile-based EEG electrode (textrode) from silver-plated polyamide loop fabric washable up to 100 cycles. The new EEG textrode collects quality signals comparable to commercial dry Ag/AgCl EEG electrodes. The signals were detected at all major EEG bandw

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Mansi, Silvia Angela, Reza Jamali, Gianmarco Battista, et al. "EEG measurements-based study for evaluating acoustic human perception: A pilot study." Acta IMEKO 13, no. 2 (2024): 1–9. http://dx.doi.org/10.21014/actaimeko.v13i2.1698.

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Sound quality analysis and sound design are well-known human-centered strategies to evaluate the subjective perception of noise and to design machines and environments with pleasant and comfortable acoustic signatures. The subjective acoustic perception is conventionally measured by means of sound quality metrics determined through a correlation process with jury test results. The exploitation of electroencephalogram (EEG) measurements during the jury test for the registration of the brain activity in response to the acoustic stimuli presented to the jurors can allow us to estimate the jurors’

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Mohamed, Manal, Nourelhuda Mohamed, and Jae Gwan Kim. "Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review." Biosensors 13, no. 12 (2023): 1019. http://dx.doi.org/10.3390/bios13121019.

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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

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Shi, Weiwei, Jinyong Zhang, Zhiguo Zhang, Lizhi Hu, and Yongqian Su. "An introduction and review on innovative silicon implementations of implantable/scalp EEG chips for data acquisition, seizure/behavior detection, and brain stimulation." Brain Science Advances 6, no. 3 (2020): 242–54. http://dx.doi.org/10.26599/bsa.2020.9050024.

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Technological advances in the semiconductor industry and the increasing demand and development of wearable medical systems have enabled the development of dedicated chips for complex electroencephalogram (EEG) signal processing with smart functions and artificial intelligence‐based detections/classifications. Around 10 million transistors are integrated into a 1 mm2 silicon wafer surface in the dedicated chip, making wearable EEG systems a powerful dedicated processor instead of a wireless raw data transceiver. The reduction of amplifiers and analog‐digital converters on the silicon surface ma

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Suwannarat, Arpa, Setha Pan-ngum, and Pasin Israsena. "Analysis of Minimal Channel Electroencephalography for Wearable Brain–Computer Interface." Electronics 13, no. 3 (2024): 565. http://dx.doi.org/10.3390/electronics13030565.

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Electroencephalography (EEG)-based brain—computer interface (BCI) is a non-invasive technology with potential in various healthcare applications, including stroke rehabilitation and neuro-feedback training. These applications typically require multi-channel EEG. However, setting up a multi-channel EEG headset is time-consuming, potentially resulting in patient reluctance to use the system despite its potential benefits. Therefore, we investigated the appropriate number of electrodes required for a successful BCI application in wearable devices using various numbers of EEG channels. EEG multi-f

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Lee, Soo-Bin, Ji-Won Kwon, Sahyun Sung, Seong-Hwan Moon, and Byung Ho Lee. "Delirium after Spinal Surgery: A Pilot Study of Electroencephalography Signals from a Wearable Device." Applied Sciences 12, no. 19 (2022): 9899. http://dx.doi.org/10.3390/app12199899.

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Postoperative delirium after spinal surgery in elderly patients has been a recent concern. However, there has not been a study of delirium after spinal surgery based on electroencephalography (EEG) signals from a compact wearable device. We aimed to analyze differences in EEG signals from a wearable device in patients with and without delirium after spinal surgery. Thirty-seven patients who underwent cervical or lumbar decompression and instrumented fusion for degenerative spinal disease were included. EEG waves were collected from a compact wearable device, and percentage changes from baselin

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Lin, Chin-Teng, Chen-Yu Wang, Kuan-Chih Huang, Shi-Jinn Horng, and Lun-De Liao. "Wearable, Multimodal, Biosignal Acquisition System for Potential Critical and Emergency Applications." Emergency Medicine International 2021 (June 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/9954669.

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For emergency or intensive-care units (ICUs), patients with unclear consciousness or unstable hemodynamics often require aggressive monitoring by multiple monitors. Complicated pipelines or lines increase the burden on patients and inconvenience for medical personnel. Currently, many commercial devices provide related functionalities. However, most devices measure only one biological signal, which can increase the budget for users and cause difficulty in remote integration. In this study, we develop a wearable device that integrates electrocardiography (ECG), electroencephalography (EEG), and

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Hernández-Capistrán, Jonathan, Giner Alor-Hernández, Humberto Marín-Vega, Maritza Bustos-López, Laura Nely Sanchez-Morales, and Jose Luis Sanchez-Cervantes. "Commercial Wearables for the Management of People with Autism Spectrum Disorder: A Review." Biosensors 14, no. 11 (2024): 556. http://dx.doi.org/10.3390/bios14110556.

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Autism Spectrum Disorder (ASD) necessitates comprehensive management, addressing complex challenges in social communication, behavioral regulation, and sensory processing, for which wearable technologies offer valuable tools to monitor and support interventions. Therefore, this review explores recent advancements in wearable technology, categorizing devices based on executive function, psychomotor skills, and the behavioral/emotional/sensory domain, highlighting their potential to improve ongoing management and intervention. To ensure rigor and comprehensiveness, the review employs a PRISMA-ba

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Oguni, Hirokazu. "Wearable EEG Device for Continuous Spike–Wave in Sleep." Pediatric Neurology Briefs 34 (December 2, 2020): 10. http://dx.doi.org/10.15844/pedneurbriefs-34-10.

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Chin-Teng Lin, Chun-Hsiang Chuang, Chih-Sheng Huang, et al. "Wireless and Wearable EEG System for Evaluating Driver Vigilance." IEEE Transactions on Biomedical Circuits and Systems 8, no. 2 (2014): 165–76. http://dx.doi.org/10.1109/tbcas.2014.2316224.

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Athavipach, Pan-ngum, and Israsena. "A Wearable In-Ear EEG Device for Emotion Monitoring." Sensors 19, no. 18 (2019): 4014. http://dx.doi.org/10.3390/s19184014.

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For future healthcare applications, which are increasingly moving towards out-of-hospital or home-based caring models, the ability to remotely and continuously monitor patients’ conditions effectively are imperative. Among others, emotional state is one of the conditions that could be of interest to doctors or caregivers. This paper discusses a preliminary study to develop a wearable device that is a low cost, single channel, dry contact, in-ear EEG suitable for non-intrusive monitoring. All aspects of the designs, engineering, and experimenting by applying machine learning for emotion classif

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Li, Gang, Boon-Leng Lee, and Wan-Young Chung. "Smartwatch-Based Wearable EEG System for Driver Drowsiness Detection." IEEE Sensors Journal 15, no. 12 (2015): 7169–80. http://dx.doi.org/10.1109/jsen.2015.2473679.

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Swinnen, Lauren, and Wim Van Paesschen. "Detectie van typische absences met een EEG-gebaseerde wearable." Epilepsie, periodiek voor professionals 21, no. 2 (2022): 11–14. http://dx.doi.org/10.54160/epilepsie.12255.

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Accurate bepaling van de aanvalsfrequentie bij patiënten met absence epilepsie is van groot belang voor een correcte behandeling. In het huidige beleid wordt deze keuze gemaakt op basis van de absences die gerapporteerd zijn in het aanvalsdagboek. Onderrapportage van aanvallen is echter een gekend probleem in epilepsie. Objectievere meting kan bekomen worden met een wearable seizure detection device. Onderstaande beschrijft de meting van absences met een discreet, EEG-gebaseerd apparaatje, genaamd de Sensor Dot.

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Chen, Yun-Hsuan, and Mohamad Sawan. "Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction." Sensors 21, no. 2 (2021): 460. http://dx.doi.org/10.3390/s21020460.

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We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining elect

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Chen, Yun-Hsuan, and Mohamad Sawan. "Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction." Sensors 21, no. 2 (2021): 460. http://dx.doi.org/10.3390/s21020460.

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We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining elect

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Imtiaz, Syed Anas. "A Systematic Review of Sensing Technologies for Wearable Sleep Staging." Sensors 21, no. 5 (2021): 1562. http://dx.doi.org/10.3390/s21051562.

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Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are i

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Kartsch, Victor Javier, Velu Prabhakar Kumaravel, Simone Benatti, et al. "Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis." Sensors 22, no. 24 (2022): 9803. http://dx.doi.org/10.3390/s22249803.

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Recent studies show that the integrity of core perceptual and cognitive functions may be tested in a short time with Steady-State Visual Evoked Potentials (SSVEP) with low stimulation frequencies, between 1 and 10 Hz. Wearable EEG systems provide unique opportunities to test these brain functions on diverse populations in out-of-the-lab conditions. However, they also pose significant challenges as the number of EEG channels is typically limited, and the recording conditions might induce high noise levels, particularly for low frequencies. Here we tested the performance of Normalized Canonical

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Iammarino, Erica, Ilaria Marcantoni, Agnese Sbrollini, MHD Jafar Mortada, Micaela Morettini, and Laura Burattini. "Scalp Electroencephalogram-Derived Involvement Indexes during a Working Memory Task Performed by Patients with Epilepsy." Sensors 24, no. 14 (2024): 4679. http://dx.doi.org/10.3390/s24144679.

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Electroencephalography (EEG) wearable devices are particularly suitable for monitoring a subject’s engagement while performing daily cognitive tasks. EEG information provided by wearable devices varies with the location of the electrodes, the suitable location of which can be obtained using standard multi-channel EEG recorders. Cognitive engagement can be assessed during working memory (WM) tasks, testing the mental ability to process information over a short period of time. WM could be impaired in patients with epilepsy. This study aims to evaluate the cognitive engagement of nine patients wi

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Lombardi, Ilaria, Mario Buono, Giovanna Giugliano, Vincenzo Paolo Senese, and Sonia Capece. "Usability and Acceptance Analysis of Wearable BCI Devices." Applied Sciences 15, no. 7 (2025): 3512. https://doi.org/10.3390/app15073512.

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In the current scientific and technological scenario, wearable neuroimaging devices represent a revolution in neuroscience and wearable technology. These tools combine the features of neuroimaging technologies with the convenience of wearable devices, enabling real-time exploration of brain activity in real-world contexts. This convergence defines new perspectives in scientific research, medical diagnosis, and human performance analysis. Technologies such as EEG and fNIRS enable the non-invasive monitoring of brain activity without the need for heavy clinical equipment. Indeed, miniaturization

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Mascia, Antonello, Riccardo Collu, Andrea Spanu, Matteo Fraschini, Massimo Barbaro, and Piero Cosseddu. "Wearable System Based on Ultra-Thin Parylene C Tattoo Electrodes for EEG Recording." Sensors 23, no. 2 (2023): 766. http://dx.doi.org/10.3390/s23020766.

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In an increasingly interconnected world, where electronic devices permeate every aspect of our lives, wearable systems aimed at monitoring physiological signals are rapidly taking over the sport and fitness domain, as well as biomedical fields such as rehabilitation and prosthetics. With the intent of providing a novel approach to the field, in this paper we discuss the development of a wearable system for the acquisition of EEG signals based on a portable, low-power custom PCB specifically designed to be used in combination with non-conventional ultra-conformable and imperceptible Parylene-C

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