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Relevant bibliographies by topics / Wearable EEG

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Academic literature on the topic 'Wearable EEG'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Wearable EEG"

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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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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Dissertations / Theses on the topic "Wearable EEG"

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Lovelace, Joseph A. "Ambulatory EEG Platform." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479816584544204.

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Kelly, Graham. "Development of a compact, low-cost wireless device for biopotential acquisition." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3559.

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A low-cost circuit board design is presented, which in one embodiment is smaller than a credit card, for biopotential (EMG, ECG, or EEG) data acquisition, with a focus on EEG for brain-computer interface applications. The device combines signal conditioning, low-noise and high-resolution analog-to-digital conversion of biopotentials, user motion detection via accelerometer and gyroscope, user-programmable digital pre-processing, and data transmission via Bluetooth communications. The full development of the device to date is presented, spanning three embodiments. The device is presented both a

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Imtiaz, Syed Anas. "Low-complexity algorithms for automatic detection of sleep stages and events for use in wearable EEG systems." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/29459.

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Objective: Diagnosis of sleep disorders is an expensive procedure that requires performing a sleep study, known as polysomnography (PSG), in a controlled environment. This study monitors the neural, eye and muscle activity of a patient using electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) signals which are then scored in to different sleep stages. Home PSG is often cited as an alternative of clinical PSG to make it more accessible, however it still requires patients to use a cumbersome system with multiple recording channels that need to be precisely placed. This th

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Banville, Hubert. "Enabling real-world EEG applications with deep learning." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG005.

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Au cours des dernières décennies, les avancées révolutionnaires en neuroimagerie ont permis de considérablement améliorer notre compréhension du cerveau. Aujourd'hui, avec la disponibilité croissante des dispositifs personnels de neuroimagerie portables, tels que l'EEG mobile " à bas prix ", une nouvelle ère s’annonce où cette technologie n'est plus limitée aux laboratoires de recherche ou aux contextes cliniques. Les applications de l’EEG dans le " monde réel " présentent cependant leur lot de défis, de la rareté des données étiquetées à la qualité imprévisible des signaux et leur résolution

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Grosselin, Fanny. "Apprentissage neuronal par neurofeedback à l’aide d’un système EEG portable : application à la réduction du stress chez l'Homme." Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS125.pdf.

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Cette thèse porte sur la conception, l’implémentation et l’évaluation d’un système de neurofeedback EEG portable, d’aide à la gestion du stress, à destination du grand public. Un tel système permet aux utilisateurs d’apprendre à moduler leurs états mentaux par des phénomènes de plasticité cérébrale. Cependant, plusieurs facteurs peuvent compliquer cet apprentissage, comme un plus faible rapport signal sur bruit de l'EEG acquis par des électrodes sèches, la contamination par des artefacts ou encore la définition de paramètres pertinents à partir des signaux EEG. Afin d’optimiser ce retour neuro

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Sarraf, Niloufar. "Mapping the neural activities and affective dimensions of the ISP model: Correlates in the search exploration, formulation, and collection stages." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/127009/2/Niloufar_Sarraf_Thesis.pdf.

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The affective and neurological components of information retrieval system design have increasingly become an essential part of research in human-information interaction and interactive information retrieval. These sophisticated processes are composed of not only human cognitive processes but also emotional and neuropsychological responses. This research contributes three original findings to the field of Information Science, positioned in Neuro Information Science. This experimental research 1) mapped the neurophysiological dimensions of information search processes model, 2) integrated the th

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Albaba, Adnan. "MODEL-BASED ECG ANALYSIS:TOWARDS PATIENT-SPECIFIC WEARABLE ECG MONITORING : MODEL-BASED ECG ANALYSIS:TOWARDS PATIENT-SPECIFIC WEARABLE ECG MONITORING." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-409069.

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In this thesis, model-based analysis approach is considered as a possible solution towards a patient-specific point-of-care device for the purpose of electrocardiogram monitoring. Two novel methods are proposed, tested, and quantitatively evaluated. First, a method for estimating the instantaneous heart rate using the morphologicalfeatures of one electrocardiogram beat at a time is proposed. This work is not aimed at introducing an alternative way for heart rate estimation, but rather illustrates the utility of model-basedelectrocardiogram analysis in online individualized monitoring ofthe hea

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Taji, Bahareh. "Signal Quality Assessment in Wearable ECG Devices." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/38851.

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There is a current trend towards the use of wearable biomedical devices for the purpose of recording various biosignals, such as electrocardiograms (ECG). Wearable devices have different issues and challenges compared to nonwearable ones, including motion artifacts and contact characteristics related to body-conforming materials. Due to this susceptibility to noise and artifacts, signals acquired from wearable devices may lead to incorrect interpretations, including false alarms and misdiagnoses. This research addresses two challenges of wearable devices. First, it investigates the effect of a

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Delano, Margaret K. "A long term wearable electrocardiogram (ECG) measurement system." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/76811.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 101-102).<br>In this thesis, a low-power, wearable monitoring system was developed from discrete electronic components and custom PCBs. The device was designed to maximize comfort and minimize the footprint on the user. A single le

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He, David Da. "A wearable heart monitor at the ear using ballistocardiogram (BCG) and electrocardiogram (ECG) with a nanowatt ECG heartbeat detection circuit." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/79221.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 132-137).<br>This work presents a wearable heart monitor at the ear that uses the ballistocardiogram (BCG) and the electrocardiogram (ECG) to extract heart rate, stroke volume, and pre-ejection period (PEP) for the application of continuous heart monitoring. Being a natural anchoring point, the ear is demonstrated as a viable location for the integrated sensing of physiological signals. The source

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Books on the topic "Wearable EEG"

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Xu, Jiawei, Refet Firat Yazicioglu, Chris Van Hoof, and Kofi Makinwa. Low Power Active Electrode ICs for Wearable EEG Acquisition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74863-4.

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Chaudhuri, Subhasis, Tanmay D. Pawar, and Siddhartha Duttagupta. Ambulation Analysis in Wearable ECG. Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0724-0.

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Hoof, Chris Van, Jiawei Xu, Refet Firat Yazicioglu, and Kofi Makinwa. Low Power Active Electrode ICs for Wearable EEG Acquisition. Springer, 2018.

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Hoof, Chris Van, Jiawei Xu, Refet Firat Yazicioglu, and Kofi Makinwa. Low Power Active Electrode ICs for Wearable EEG Acquisition. Springer, 2019.

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Gargiulo, Ludovica, Antonio Esposito, Pasquale Arpaia, and Nicola Moccaldi. Wearable Brain-Computer Interfaces: Prototyping EEG-Based Instruments for Monitoring and Control. Taylor & Francis Group, 2023.

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Gargiulo, Ludovica, Antonio Esposito, Pasquale Arpaia, and Nicola Moccaldi. Wearable Brain-Computer Interfaces: Prototyping EEG-Based Instruments for Monitoring and Control. Taylor & Francis Group, 2023.

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Gargiulo, Ludovica, Antonio Esposito, Pasquale Arpaia, and Nicola Moccaldi. Wearable Brain-Computer Interfaces: Prototyping EEG-Based Instruments for Monitoring and Control. Taylor & Francis Group, 2023.

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Wearable Brain-Computer Interfaces: Prototyping EEG-Based Instruments for Monitoring and Control. CRC Press LLC, 2023.

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Chaudhuri, Subhasis, Tanmay D. Pawar, and Siddhartha Duttagupta. Ambulation Analysis in Wearable ECG. Springer, 2010.

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Chaudhuri, Subhasis, Tanmay D. Pawar, and Siddhartha Duttagupta. Ambulation Analysis in Wearable ECG. Springer, 2009.

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Book chapters on the topic "Wearable EEG"

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Arpaia, Pasquale, Antonio Esposito, Ludovica Gargiulo, and Nicola Moccaldi. "EEG-Based Monitoring Instrumentation." In Wearable Brain-Computer Interfaces. CRC Press, 2023. http://dx.doi.org/10.1201/9781003263876-7.

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Yang, Rong, Hui-qun Fu, Xiu-feng Zhang, Li Wang, Ning Zhang, and Feng-ling Ma. "Design and Application of Auditory Evoked EEG Processing Platform Based on Matlab." In Wearable Sensors and Robots. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2404-7_34.

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Looney, David, Preben Kidmose, and Danilo P. Mandic. "Ear-EEG: User-Centered and Wearable BCI." In Brain-Computer Interface Research. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54707-2_5.

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Fong, Geanne, Jee-Hou Ho, Jong Chern Lim, and Bee Ting Chan. "Human Mental Stress Evaluation Using Wearable EEG and ECG Sensors." In Lecture Notes in Bioengineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1920-4_4.

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Li, Jian, Hui-qun Fu, Xiu-feng Zhang, et al. "Research and Development for Upper Limb Amputee Training System Based on EEG and VR." In Wearable Sensors and Robots. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2404-7_21.

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Wang, Li, Huiqun Fu, Xiu-feng Zhang, Rong Yang, Ning Zhang, and Fengling Ma. "An Adaptive Feature Extraction and Classification Method of Motion Imagery EEG Based on Virtual Reality." In Wearable Sensors and Robots. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2404-7_8.

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Verwulgen, Stijn, Daniel Lacko, Hoppenbrouwers Justine, et al. "Determining Comfortable Pressure Ranges for Wearable EEG Headsets." In Advances in Human Factors in Wearable Technologies and Game Design. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94619-1_2.

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Zhao, Shengjie, Qinglin Zhao, Xiaowei Zhang, et al. "Wearable EEG-Based Real-Time System for Depression Monitoring." In Brain Informatics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70772-3_18.

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Xu, Jiawei, Rachit Mohan, Nick Van Helleputte, and Srinjoy Mitra. "Design and Optimization of ICs for Wearable EEG Sensors." In CMOS Circuits for Biological Sensing and Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67723-1_7.

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Bisoni, Lorenzo, Enzo Mastinu, and Massimo Barbaro. "A Wearable Device for High-Frequency EEG Signal Recording." In Biomedical Engineering Systems and Technologies. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27707-3_5.

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Conference papers on the topic "Wearable EEG"

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Chaichanasittikarn, Oranatt, Jeremy Lin Weixuan, Manuel Seet, et al. "Neural Mechanisms of Malodor Masking: A Wearable EEG Study." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10781996.

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Ingolfsson, Thorir Mar, Victor Javier Kartsch Morinigo, Andrea Cossettini, Xiaying Wang, and Luca Benini. "VowelNet: Enhancing Communication with Wearable EEG-Based Vowel Imagery." In 2024 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2024. https://doi.org/10.1109/biocas61083.2024.10798267.

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Patel, Aviral, Prerna Zagade, Shravan Ambudkar, Aboli Jadhav, and Bhavna Ambudkar. "Innovative Wearable EEG Solution for Stress Assessment and Control." In 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA). IEEE, 2024. https://doi.org/10.1109/icisaa62385.2024.10828600.

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Nithya, T., V. Ezhilkumar, K. S. Hari Supikssa, S. Harshini, and R. Kishore. "Emotion Recognition from Wearable EEG Devices Using Neural Networks." In 2024 13th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2024. https://doi.org/10.1109/smart63812.2024.10882207.

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Viola, Fabrizio Antonio, Andrea Spanu, Antonio Dominguez Alfaro, Miryam Criado-Gonzalez, David Mecerreyes, and Annalisa Bonfiglio. "A Wearable EEG Band Based on Spray-Coated Textile Bioelectrodes." In 2024 IEEE SENSORS. IEEE, 2024. https://doi.org/10.1109/sensors60989.2024.10784522.

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Goh, Teck Lun, and Li-Shiuan Peh. "Demo: WalkingWizard - A Truly Wearable EEG Headwear for Everyday Use." In 2024 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). IEEE, 2024. http://dx.doi.org/10.1109/chase60773.2024.00044.

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Xue, Ruijie, Weiwei Shi, and Yang Hong. "A Configurable EEG Procesing Driver Chip for Wireless Wearable Devices." In 2024 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA). IEEE, 2024. https://doi.org/10.1109/icta64028.2024.10860440.

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Sahoo, Girija Shankar, Sorabh Sharma, Subash Chandra Tripathy, Abhishek Singla, V. Pushparajesh, and Azra Jeelani. "Real-Time Monitoring of Sleep Patterns Using Wearable EEG Sensors." In 2025 International Conference on Automation and Computation (AUTOCOM). IEEE, 2025. https://doi.org/10.1109/autocom64127.2025.10956367.

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Romaniuk, Vladimir, and Alexey Kashevnik. "Eye Movement Assessment Methodology Based on Wearable EEG Headband Data Analysis." In 2024 36th Conference of Open Innovations Association (FRUCT). IEEE, 2024. http://dx.doi.org/10.23919/fruct64283.2024.10749882.

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Mai, Ngoc-Dau, Kentaro Go, Xiaoyang Mao, and Wan-Young Chung. "Wearable Ear EEG Device for Emotion Recognition in Human-Robot Interaction." In 2024 International Conference on Cyberworlds (CW). IEEE, 2024. https://doi.org/10.1109/cw64301.2024.00045.

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Reports on the topic "Wearable EEG"

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Duann, Jeng-Ren, Tzyy-Ping Jung, and Jin-Chern Chiou. Concurrent EEG And NIRS Tomographic Imaging Based on Wearable Electro-Optodes. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada603612.

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Evans, Jon, Ian Porter, Emma Cockcroft, Al-Amin Kassam, and Jose Valderas. Collecting linked patient reported and technology reported outcome measures for informing clinical decision making: a scoping review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.10.0038.

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Review question / Objective: We aim to map out the existing research where concomitant use of patient reported and technology reported outcome measures is used for patients with musculoskeletal conditions. Condition being studied: Musculoskeletal disorders (MSD) covering injuries or disorders of the muscles, nerves, tendons, joints, cartilage, and spinal discs. Musculoskeletal manifestations of joint pathology. Eligibility criteria: 1) Peer-reviewed primary studies and literature reviews. Grey literature not included. 2) Studies which include co-administration of Patient-Reported Outcomes (PRO

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Treadwell, Jonathan R., James T. Reston, Benjamin Rouse, Joann Fontanarosa, Neha Patel, and Nikhil K. Mull. Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes. Agency for Healthcare Research and Quality (AHRQ), 2021. http://dx.doi.org/10.23970/ahrqepctb38.

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Background. Automated-entry consumer devices that collect and transmit patient-generated health data (PGHD) are being evaluated as potential tools to aid in the management of chronic diseases. The need exists to evaluate the evidence regarding consumer PGHD technologies, particularly for devices that have not gone through Food and Drug Administration evaluation. Purpose. To summarize the research related to automated-entry consumer health technologies that provide PGHD for the prevention or management of 11 chronic diseases. Methods. The project scope was determined through discussions with Ke

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