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Vitali D, Olugbade T, Eccleston C, Keogh E, Bianchi-Berthouze N, de C Williams AC. Sensing behavior change in chronic pain: a scoping review of sensor technology for use in daily life. Pain 2024; 165:1348-1360. [PMID: 38258888 DOI: 10.1097/j.pain.0000000000003134] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/26/2023] [Indexed: 01/24/2024]
Abstract
ABSTRACT Technology offers possibilities for quantification of behaviors and physiological changes of relevance to chronic pain, using wearable sensors and devices suitable for data collection in daily life contexts. We conducted a scoping review of wearable and passive sensor technologies that sample data of psychological interest in chronic pain, including in social situations. Sixty articles met our criteria from the 2783 citations retrieved from searching. Three-quarters of recruited people were with chronic pain, mostly musculoskeletal, and the remainder with acute or episodic pain; those with chronic pain had a mean age of 43 (few studies sampled adolescents or children) and 60% were women. Thirty-seven studies were performed in laboratory or clinical settings and the remainder in daily life settings. Most used only 1 type of technology, with 76 sensor types overall. The commonest was accelerometry (mainly used in daily life contexts), followed by motion capture (mainly in laboratory settings), with a smaller number collecting autonomic activity, vocal signals, or brain activity. Subjective self-report provided "ground truth" for pain, mood, and other variables, but often at a different timescale from the automatically collected data, and many studies reported weak relationships between technological data and relevant psychological constructs, for instance, between fear of movement and muscle activity. There was relatively little discussion of practical issues: frequency of sampling, missing data for human or technological reasons, and the users' experience, particularly when users did not receive data in any form. We conclude the review with some suggestions for content and process of future studies in this field.
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Affiliation(s)
- Diego Vitali
- Research Department of Clinical, Educational & Health Psychology, University College London, London, United Kingdom
| | - Temitayo Olugbade
- School of Engineering and Informatics, University of Sussex, Brighton, United Kingdom
- Interaction Centre, University College London, London, United Kingdom
| | - Christoper Eccleston
- Centre for Pain Research, The University of Bath, Bath, United Kingdom
- Department of Experimental, Clinical and Health Psychology, Ghent University, Ghent, Belgium
- Department of Psychology, The University of Helsinki, Helsinki, Finland
| | - Edmund Keogh
- Centre for Pain Research, The University of Bath, Bath, United Kingdom
| | | | - Amanda C de C Williams
- Research Department of Clinical, Educational & Health Psychology, University College London, London, United Kingdom
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Manoharan AK, Batcha MIK, Mahalingam S, Raj B, Kim J. Recent Advances in Two-Dimensional Nanomaterials for Healthcare Monitoring. ACS Sens 2024; 9:1706-1734. [PMID: 38563358 DOI: 10.1021/acssensors.4c00015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The development of advanced technologies for the fabrication of functional nanomaterials, nanostructures, and devices has facilitated the development of biosensors for analyses. Two-dimensional (2D) nanomaterials, with unique hierarchical structures, a high surface area, and the ability to be functionalized for target detection at the surface, exhibit high potential for biosensing applications. The electronic properties, mechanical flexibility, and optical, electrochemical, and physical properties of 2D nanomaterials can be easily modulated, enabling the construction of biosensing platforms for the detection of various analytes with targeted recognition, sensitivity, and selectivity. This review provides an overview of the recent advances in 2D nanomaterials and nanostructures used for biosensor and wearable-sensor development for healthcare and health-monitoring applications. Finally, the advantages of 2D-nanomaterial-based devices and several challenges in their optimal operation have been discussed to facilitate the development of smart high-performance biosensors in the future.
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Affiliation(s)
- Arun Kumar Manoharan
- Department of Electrical, Electronics and Communication Engineering, School of Technology, Gandhi Institute of Technology and Management (GITAM), Bengaluru 561203, Karnataka, India
| | - Mohamed Ismail Kamal Batcha
- Department of Electronics and Communication Engineering, Agni College of Technology, Chennai 600130, Tamil Nadu, India
| | - Shanmugam Mahalingam
- Department of Materials System Engineering, Pukyong National University, Busan 48513, Republic of Korea
| | - Balwinder Raj
- Department of Electronics and Communication Engineering, Dr B R Ambedkar National Institute of Technology Jalandhar, Punjab 144011, India
| | - Junghwan Kim
- Department of Materials System Engineering, Pukyong National University, Busan 48513, Republic of Korea
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Zeydabadinezhad M, Jowers J, Buhl D, Cabaniss B, Mahmoudi B. A personalized earbud for non-invasive long-term EEG monitoring. J Neural Eng 2024; 21:026026. [PMID: 38479008 DOI: 10.1088/1741-2552/ad33af] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 03/13/2024] [Indexed: 04/05/2024]
Abstract
Objective. The primary objective of this study was to evaluate the reliability, comfort, and performance of a custom-fit, non-invasive long-term electrophysiologic headphone, known as Aware Hearable, for the ambulatory recording of brain activities. These recordings play a crucial role in diagnosing neurological disorders such as epilepsy and in studying neural dynamics during daily activities.Approach.The study uses commercial manufacturing processes common to the hearing aid industry, such as 3D scanning, computer-aided design modeling, and 3D printing. These processes enable the creation of the Aware Hearable with a personalized, custom-fit, thereby ensuring complete and consistent contact with the inner surfaces of the ear for high-quality data recordings. Additionally, the study employs a machine learning data analysis approach to validate the recordings produced by Aware Hearable, by comparing them to the gold standard intracranial electroencephalography recordings in epilepsy patients.Main results.The results indicate the potential of Aware Hearable to expedite the diagnosis of epilepsy by enabling extended periods of ambulatory recording.Significance.This offers significant reductions in burden to patients and their families. Furthermore, the device's utility may extend to a broader spectrum, making it suitable for other applications involving neurophysiological recordings in real-world settings.
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Affiliation(s)
- Mahmoud Zeydabadinezhad
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
| | - Jon Jowers
- United Sciences, LLC, Atlanta, GA, United States of America
| | - Derek Buhl
- Takeda Pharmaceuticals Company Limited, Cambridge, MA, United States of America
| | - Brian Cabaniss
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, United States of America
| | - Babak Mahmoudi
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA, United States of America
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Wang Z, Ding Y, Yuan W, Chen H, Chen W, Chen C. Active Claw-Shaped Dry Electrodes for EEG Measurement in Hair Areas. Bioengineering (Basel) 2024; 11:276. [PMID: 38534550 DOI: 10.3390/bioengineering11030276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/01/2024] [Accepted: 03/07/2024] [Indexed: 03/28/2024] Open
Abstract
EEG, which can provide brain alteration information via recording the electrical activity of neurons in the cerebral cortex, has been widely used in neurophysiology. However, conventional wet electrodes in EEG monitoring typically suffer from inherent limitations, including the requirement of skin pretreatment, the risk of superficial skin infections, and signal performance deterioration that may occur over time due to the air drying of the conductive gel. Although the emergence of dry electrodes has overcome these shortcomings, their electrode-skin contact impedance is significantly high and unstable, especially in hair-covered areas. To address the above problems, an active claw-shaped dry electrode is designed, moving from electrode morphological design, slurry preparation, and coating to active electrode circuit design. The active claw-shaped dry electrode, which consists of a claw-shaped electrode and active electrode circuit, is dedicated to offering a flexible solution for elevating electrode fittings on the scalp in hair-covered areas, reducing electrode-skin contact impedance and thus improving the quality of the acquired EEG signal. The performance of the proposed electrodes was verified by impedance, active electrode circuit, eyes open-closed, steady-state visually evoked potential (SSVEP), and anti-interference tests, based on EEG signal acquisition. Experimental results show that the proposed claw-shaped electrodes (without active circuit) can offer a better fit between the scalp and electrodes, with a low electrode-skin contact impedance (18.62 KΩ@1 Hz in the hairless region and 122.15 KΩ@1 Hz in the hair-covered region). In addition, with the active circuit, the signal-to-noise ratio (SNR) of the acquiring EEG signal was improved and power frequency interference was restrained, therefore, the proposed electrodes can yield an EEG signal quality comparable to wet electrodes.
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Affiliation(s)
- Zaihao Wang
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Yuhao Ding
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Wei Yuan
- Center for Intelligent Medical Equipment and Devices, Institute for Innovative Medical Devices, School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China
| | - Hongyu Chen
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Wei Chen
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia
| | - Chen Chen
- Human Phenome Institute, Fudan University, Shanghai 201203, China
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5
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Triana AM, Saramäki J, Glerean E, Hayward NMEA. Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping. Hum Brain Mapp 2024; 45:e26620. [PMID: 38436603 PMCID: PMC10911114 DOI: 10.1002/hbm.26620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 03/05/2024] Open
Abstract
A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.
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Affiliation(s)
- Ana María Triana
- Department of Computer Science, School of ScienceAalto UniversityEspooFinland
| | - Jari Saramäki
- Department of Computer Science, School of ScienceAalto UniversityEspooFinland
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, School of ScienceAalto UniversityEspooFinland
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Irrera F, Gumiero A, Zampogna A, Boscari F, Avogaro A, Gazzanti Pugliese di Cotrone MA, Patera M, Della Torre L, Picozzi N, Suppa A. Multisensor Integrated Platform Based on MEMS Charge Variation Sensing Technology for Biopotential Acquisition. SENSORS (BASEL, SWITZERLAND) 2024; 24:1554. [PMID: 38475089 DOI: 10.3390/s24051554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/25/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024]
Abstract
We propose a new methodology for long-term biopotential recording based on an MEMS multisensor integrated platform featuring a commercial electrostatic charge-transfer sensor. This family of sensors was originally intended for presence tracking in the automotive industry, so the existing setup was engineered for the acquisition of electrocardiograms, electroencephalograms, electrooculograms, and electromyography, designing a dedicated front-end and writing proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in a domestic environment will be discussed in detail. The excellent results indicate that this technology can provide a low-power, unexplored solution to biopotential acquisition. The technological breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, it opens up additional possibilities for wearable sensors and strengthens the role of MEMS technology in medical wearables for the long-term synchronous acquisition of a wide range of signals.
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Affiliation(s)
- Fernanda Irrera
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00185 Rome, Italy
| | | | - Alessandro Zampogna
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
| | | | - Angelo Avogaro
- Department of Medicine, University of Padua, 35122 Padua, Italy
| | | | - Martina Patera
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
| | | | | | - Antonio Suppa
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
- IRCCS Neuromed, 86077 Pozzilli, Italy
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7
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Şekerci Y, Kahraman MU, Özturan Ö, Çelik E, Ayan SŞ. Neurocognitive responses to spatial design behaviors and tools among interior architecture students: a pilot study. Sci Rep 2024; 14:4454. [PMID: 38396070 PMCID: PMC10891056 DOI: 10.1038/s41598-024-55182-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 02/21/2024] [Indexed: 02/25/2024] Open
Abstract
The impact of emotions on human behavior is substantial, and the ability to recognize people's feelings has a wide range of practical applications including education. Here, the methods and tools of education are being calibrated according to the data gained over electroencephalogram (EEG) signals. The issue of which design tools would be ideal in the future of interior architecture education, is an uncertain field. It is important to measure the students' emotional states while using manual and digital design tools to determine the different impacts. Brain-computer interfaces have made it possible to monitor emotional states in a way that is both convenient and economical. In the research of emotion recognition, EEG signals have been employed, and the resulting literature explains basic emotions as well as complicated scenarios that are created from the combination of numerous basic emotions. The objective of this study is to investigate the emotional states and degrees of attachment experienced by interior architecture students while engaging in their design processes. This includes examining the use of 2D or 3D tools, whether manual or digital, and identifying any changes in design tool usage and behaviors that may be influenced by different teaching techniques. Accordingly, the hierarchical clustering which is a technique used in data analysis to group objects into a hierarchical structure of clusters based on their similarities has been conducted.
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Affiliation(s)
- Yaren Şekerci
- Interior Architecture and Environmental Design, Antalya Bilim University, Antalya, 07190, Turkey.
| | - Mehmet Uğur Kahraman
- Interior Architecture and Environmental Design, Antalya Bilim University, Antalya, 07190, Turkey
| | - Özgü Özturan
- Akdeniz University, Interior Architecture, Antalya, 07070, Turkey
| | - Ertuğrul Çelik
- Electrical and Computer Engineering, Antalya Bilim University, Antalya, 07190, Turkey
| | - Sevgi Şengül Ayan
- Industrial Engineering, Antalya Bilim University, Antalya, 07190, Turkey
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8
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Correia G, Crosse MJ, Lopez Valdes A. Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors. SENSORS (BASEL, SWITZERLAND) 2024; 24:1226. [PMID: 38400384 PMCID: PMC10893377 DOI: 10.3390/s24041226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024]
Abstract
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.
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Affiliation(s)
- Guilherme Correia
- Department of Physics, NOVA School of Science and Technology, 2829-516 Caparica, Portugal;
| | - Michael J. Crosse
- Segotia, H91 HE9E Galway, Ireland;
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, D02 R590 Dublin, Ireland
| | - Alejandro Lopez Valdes
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, D02 R590 Dublin, Ireland
- Global Brain Health Institute, Trinity College Dublin, D02 X9W9 Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, D02 X9W9 Dublin, Ireland
- Department of Electronic and Electrical Engineering, Trinity College Dublin, D02 Dublin, Ireland
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Parupelli SK, Desai S. The 3D Printing of Nanocomposites for Wearable Biosensors: Recent Advances, Challenges, and Prospects. Bioengineering (Basel) 2023; 11:32. [PMID: 38247910 PMCID: PMC10813523 DOI: 10.3390/bioengineering11010032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/11/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024] Open
Abstract
Notably, 3D-printed flexible and wearable biosensors have immense potential to interact with the human body noninvasively for the real-time and continuous health monitoring of physiological parameters. This paper comprehensively reviews the progress in 3D-printed wearable biosensors. The review also explores the incorporation of nanocomposites in 3D printing for biosensors. A detailed analysis of various 3D printing processes for fabricating wearable biosensors is reported. Besides this, recent advances in various 3D-printed wearable biosensors platforms such as sweat sensors, glucose sensors, electrocardiography sensors, electroencephalography sensors, tactile sensors, wearable oximeters, tattoo sensors, and respiratory sensors are discussed. Furthermore, the challenges and prospects associated with 3D-printed wearable biosensors are presented. This review is an invaluable resource for engineers, researchers, and healthcare clinicians, providing insights into the advancements and capabilities of 3D printing in the wearable biosensor domain.
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Affiliation(s)
- Santosh Kumar Parupelli
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA;
- Center of Excellence in Product Design and Advanced Manufacturing, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
| | - Salil Desai
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA;
- Center of Excellence in Product Design and Advanced Manufacturing, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
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Arao H, Suwazono S, Kimura A, Asano H, Suzuki H. Measuring auditory event-related potentials at the external ear canal: A demonstrative study using a new electrode and error-feedback paradigm. Eur J Neurosci 2023; 58:4310-4327. [PMID: 37875165 DOI: 10.1111/ejn.16175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 09/21/2023] [Accepted: 10/04/2023] [Indexed: 10/26/2023]
Abstract
Although ear canal electroencephalogram (EEG) recording has received interest from basic and applied research communities, evidence on how it can be implemented in practice is limited. The present study involving eight male participants including the authors presents the utility of our ear canal electrode and method by demonstrating both comparability of ear canal EEG to those at nearby sites and distinctiveness that ear canal event-related potentials (ERPs) could have. For this purpose, we used the balanced noncephalic electrode reference and an experimental paradigm with an error-feedback sound. Clear auditory ERPs were detected at the ear canal sites with a sufficiently low noise level comparable with those at conventional sites. The N1c, a temporal maximum subcomponent, spread over the bilateral temporal sites, including the ear canals and earlobes. While consecutive signals are generally highly similar between the ear canal and the earlobe, the N1c was larger at the ear canal than the earlobe, as demonstrated by the conventional frequentist and the hierarchical Bayesian modelling approaches. Although an evident caveat is that our sample was limited in terms of size and sex, the general capability indicates that the structure of our ear canal electrode provides EEG measurement that can be used in basic and applied settings. Our experimental method can also be an ERP-based test that conveniently assesses the capability of existing and future ear canal electrodes. The distinctive nature of the ERPs to the error-feedback sound may be utilized to examine the basic aspects of auditory ERPs and to test the processes involved in feedback-guided behaviour of participants.
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Affiliation(s)
- Hiroshi Arao
- Department of Human Sciences, Taisho University, Tokyo, Japan
| | - Shugo Suwazono
- Department of Neurology and Center for Clinical Neuroscience, National Hospital Organization Okinawa National Hospital, Ginowan, Japan
| | | | - Hirotoshi Asano
- Department of Computer Science, Kogakuin University, Tokyo, Japan
| | - Hiroaki Suzuki
- Department of Human Sciences, Taisho University, Tokyo, Japan
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Yang T, Zhang P, Xing L, Hu J, Feng R, Zhong J, Li W, Zhang Y, Zhu Q, Yang Y, Gao F, Qian Z. Insights into brain perceptions of the different taste qualities and hedonic valence of food via scalp electroencephalogram. Food Res Int 2023; 173:113311. [PMID: 37803622 DOI: 10.1016/j.foodres.2023.113311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 07/03/2023] [Accepted: 07/21/2023] [Indexed: 10/08/2023]
Abstract
Investigating brain activity is essential for exploring taste-experience related cues. The paper aimed to explore implicit (unconscious) emotional or physiological responses related to taste experiences using scalp electroencephalogram (EEG). We performed implicit measures of tastants of differing perceptual types (bitter, salty, sour and sweet) and intensities (low, medium, and high). The results showed that subjects were partially sensitive to different sensory intensities, i.e., for high intensities, taste stimuli could induce activation of different rhythm signals in the brain, with α and θ bands possibly being more sensitive to different taste types. Furthermore, the neural representations and corresponding sensory qualities (e.g., "sweet: pleasant" or "bitter: unpleasant") of different tastes could be discriminated at 250-1,500 ms after stimulus onset, and different tastes exhibited distinct temporal dynamic differences. Source localization indicated that different taste types activate brain areas associated with emotional eating, reward processing, and motivated tendencies, etc. Overall, our findings reveal a larger sophisticated taste map that accounted for the diversity of taste types in the human brain and assesses the emotion, reward, and motivated behavior represented by different tastes. This study provided basic insights and a perceptual foundation for the relationship between taste experience-related decisions and the prediction of brain activity.
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Affiliation(s)
- Tianyi Yang
- Department of Biomedical Engineering, Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology, Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China
| | - Peng Zhang
- Department of Biomedical Engineering, Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology, Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China
| | - Lidong Xing
- Department of Biomedical Engineering, Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology, Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China
| | - Jin Hu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, National Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Shanghai Key Lab. of Brain Function and Regeneration, Institute of Neurosurgery, Shanghai 200040, PR China
| | - Rui Feng
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, National Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Shanghai Key Lab. of Brain Function and Regeneration, Institute of Neurosurgery, Shanghai 200040, PR China
| | - Junjie Zhong
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, National Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Shanghai Key Lab. of Brain Function and Regeneration, Institute of Neurosurgery, Shanghai 200040, PR China
| | - Weitao Li
- Department of Biomedical Engineering, Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology, Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China
| | - Yizhi Zhang
- Department of Biomedical Engineering, Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology, Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China
| | - Qiaoqiao Zhu
- Department of Biomedical Engineering, Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology, Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China
| | - Yamin Yang
- Department of Biomedical Engineering, Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology, Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China
| | - Fan Gao
- Department of Biomedical Engineering, Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology, Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China.
| | - Zhiyu Qian
- Department of Biomedical Engineering, Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology, Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China.
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Di Flumeri G, Giorgi A, Germano D, Ronca V, Vozzi A, Borghini G, Tamborra L, Simonetti I, Capotorto R, Ferrara S, Sciaraffa N, Babiloni F, Aricò P. A Neuroergonomic Approach Fostered by Wearable EEG for the Multimodal Assessment of Drivers Trainees. SENSORS (BASEL, SWITZERLAND) 2023; 23:8389. [PMID: 37896483 PMCID: PMC10610858 DOI: 10.3390/s23208389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/06/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
When assessing trainees' progresses during a driving training program, instructors can only rely on the evaluation of a trainee's explicit behavior and their performance, without having any insight about the training effects at a cognitive level. However, being able to drive does not imply knowing how to drive safely in a complex scenario such as the road traffic. Indeed, the latter point involves mental aspects, such as the ability to manage and allocate one's mental effort appropriately, which are difficult to assess objectively. In this scenario, this study investigates the validity of deploying an electroencephalographic neurometric of mental effort, obtained through a wearable electroencephalographic device, to improve the assessment of the trainee. The study engaged 22 young people, without or with limited driving experience. They were asked to drive along five different but similar urban routes, while their brain activity was recorded through electroencephalography. Moreover, driving performance, subjective and reaction times measures were collected for a multimodal analysis. In terms of subjective and performance measures, no driving improvement could be detected either through the driver's subjective measures or through their driving performance. On the other side, through the electroencephalographic neurometric of mental effort, it was possible to catch their improvement in terms of mental performance, with a decrease in experienced mental demand after three repetitions of the driving training tasks. These results were confirmed by the analysis of reaction times, that significantly improved from the third repetition as well. Therefore, being able to measure when a task is less mentally demanding, and so more automatic, allows to deduce the degree of users training, becoming capable of handling additional tasks and reacting to unexpected events.
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Affiliation(s)
- Gianluca Di Flumeri
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
| | - Andrea Giorgi
- BrainSigns srl, 00198 Rome, Italy
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Daniele Germano
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, 00185 Rome, Italy
| | - Vincenzo Ronca
- BrainSigns srl, 00198 Rome, Italy
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, 00185 Rome, Italy
| | - Alessia Vozzi
- BrainSigns srl, 00198 Rome, Italy
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Gianluca Borghini
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
| | - Luca Tamborra
- BrainSigns srl, 00198 Rome, Italy
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Ilaria Simonetti
- BrainSigns srl, 00198 Rome, Italy
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Rossella Capotorto
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
| | | | | | - Fabio Babiloni
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Pietro Aricò
- BrainSigns srl, 00198 Rome, Italy
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, 00185 Rome, Italy
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13
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Kaongoen N, Choi J, Woo Choi J, Kwon H, Hwang C, Hwang G, Kim BH, Jo S. The future of wearable EEG: a review of ear-EEG technology and its applications. J Neural Eng 2023; 20:051002. [PMID: 37748474 DOI: 10.1088/1741-2552/acfcda] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 09/25/2023] [Indexed: 09/27/2023]
Abstract
Objective.This review paper provides a comprehensive overview of ear-electroencephalogram (EEG) technology, which involves recording EEG signals from electrodes placed in or around the ear, and its applications in the field of neural engineering.Approach.We conducted a thorough literature search using multiple databases to identify relevant studies related to ear-EEG technology and its various applications. We selected 123 publications and synthesized the information to highlight the main findings and trends in this field.Main results.Our review highlights the potential of ear-EEG technology as the future of wearable EEG technology. We discuss the advantages and limitations of ear-EEG compared to traditional scalp-based EEG and methods to overcome those limitations. Through our review, we found that ear-EEG is a promising method that produces comparable results to conventional scalp-based methods. We review the development of ear-EEG sensing devices, including the design, types of sensors, and materials. We also review the current state of research on ear-EEG in different application areas such as brain-computer interfaces, and clinical monitoring.Significance.This review paper is the first to focus solely on reviewing ear-EEG research articles. As such, it serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering. Our review sheds light on the exciting future prospects of ear-EEG, and its potential to advance neural engineering research and become the future of wearable EEG technology.
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Affiliation(s)
- Netiwit Kaongoen
- Information and Electronics Research Institute, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jaehoon Choi
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jin Woo Choi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304, United States of America
| | - Haram Kwon
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Chaeeun Hwang
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Guebin Hwang
- Robotics Program, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Byung Hyung Kim
- Department of Artificial Intelligence, Inha University, Incheon, Republic of Korea
| | - Sungho Jo
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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14
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Alhejaili R, Alomainy A. The Use of Wearable Technology in Providing Assistive Solutions for Mental Well-Being. SENSORS (BASEL, SWITZERLAND) 2023; 23:7378. [PMID: 37687834 PMCID: PMC10490605 DOI: 10.3390/s23177378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/08/2023] [Accepted: 08/11/2023] [Indexed: 09/10/2023]
Abstract
The main goal of this manuscript is to provide an extensive literature review and analysis of certain biomarkers, which are frequently used to identify stress, anxiety, and other emotions, leading to potential solutions for the monitoring of mental wellness using wearable technologies. It is possible to see the impacts of several biomarkers in detecting stress levels and their effectiveness with an investigation into the literature on this subject. Biofeedback training has demonstrated some psychological effects, such as a reduction in anxiety and self-control enhancement. This survey demonstrates backed up by evidence that wearable devices are assistive in providing health and mental wellness solutions. Because physical activity tracing would reduce the stress stressors, which affect the subject's body, therefore, it would also affect the mental activity and would lead to a reduction in cognitive mental load.
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Affiliation(s)
- Reham Alhejaili
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
- Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 23218, Saudi Arabia
| | - Akram Alomainy
- Antennas and Electromagnetics Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK;
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15
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Milne-Ives M, Duun-Henriksen J, Blaabjerg L, Mclean B, Shankar R, Meinert E. At home EEG monitoring technologies for people with epilepsy and intellectual disabilities: A scoping review. Seizure 2023; 110:11-20. [PMID: 37295277 DOI: 10.1016/j.seizure.2023.05.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/06/2023] [Accepted: 05/07/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND Conducting electroencephalography in people with intellectual disabilities (PwID) can be challenging, but the high proportion of PwID who experience seizures make it an essential part of their care. To reduce hospital-based monitoring, interventions are being developed to enable high-quality EEG data to be collected at home. This scoping review aims to summarise the current state of remote EEG monitoring research, potential benefits and limitations of the interventions, and inclusion of PwID in this research. METHODS The review was structured using the PRISMA extension for Scoping Reviews and the PICOS framework. Studies that evaluated a remote EEG monitoring intervention in adults with epilepsy were retrieved from the PubMed, MEDLINE, Embase, CINAHL, Web of Science, and ClinicalTrials.gov databases. A descriptive analysis provided an overview of the study and intervention characteristics, key results, strengths, and limitations. RESULTS 34,127 studies were retrieved and 23 were included. Five types of remote EEG monitoring were identified. Common benefits included producing useful results of comparable quality to inpatient monitoring and patient experience. A common limitation was the challenge of capturing all seizures with a small number of localised electrodes. No randomised controlled trials were included, few studies reported sensitivity and specificity, and only three considered PwID. CONCLUSIONS Overall, the studies demonstrated the feasibility of remote EEG interventions for out-of-hospital monitoring and their potential to improve data collection and quality of care for patients. Further research is needed on the effectiveness, benefits, and limitations of remote EEG monitoring compared to in-patient monitoring, especially for PwID.
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Affiliation(s)
- Madison Milne-Ives
- Centre for Health Technology, University of Plymouth, Plymouth, PL4 6DT, UK
| | | | | | - Brendan Mclean
- Royal Cornwall Hospitals NHS Trust, Treliske, Truro, Cornwall, TR1 3LJ, UK; Peninsula Medical School, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK; Cornwall Partnership NHS Foundation Trust, Carew House, Beacon Technology Park, Dunmere Rd, Bodmin, PL31 2QN, UK
| | - Rohit Shankar
- Peninsula Medical School, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK; Cornwall Partnership NHS Foundation Trust, Carew House, Beacon Technology Park, Dunmere Rd, Bodmin, PL31 2QN, UK
| | - Edward Meinert
- Centre for Health Technology, University of Plymouth, Plymouth, PL4 6DT, UK; Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK; Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, W6 8RP, UK.
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16
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He C, Chen YY, Phang CR, Stevenson C, Chen IP, Jung TP, Ko LW. Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG Systems Review. IEEE J Biomed Health Inform 2023; 27:3830-3843. [PMID: 37022001 DOI: 10.1109/jbhi.2023.3239053] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Wireless electroencephalography (EEG) systems have been attracting increasing attention in recent times. Both the number of articles discussing wireless EEG and their proportion relative to general EEG publications have increased over years. These trends indicate that wireless EEG systems could be more accessible to researchers and the research community has recognized the potential of wireless EEG systems. To explore the development and diverse applications of wireless EEG systems, this review highlights the trends in wearable and wireless EEG systems over the past decade and compares the specifications and research applications of the major wireless systems marketed by 16 companies. For each product, five parameters (number of channels, sampling rate, cost, battery life, and resolution) were assessed for comparison. Currently, these wearable and portable wireless EEG systems have three main application areas: consumer, clinical, and research. To address this multitude of options, the article also discussed the thought process to find a suitable device that meets personalization and use cases specificities. These investigations suggest that low-price and convenience are key factors for consumer applications, wireless EEG systems with FDA or CE-certification may be more suitable for clinical settings, and devices that provide raw EEG data with high-density channels are important for laboratory research. This article presents an overview of the current state of the wireless EEG systems specifications and possible applications and serves as a guide point as it is expected that more influential and novel research will cyclically promote the development of such EEG systems.
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17
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Alskafi FA, Khandoker AH, Marzbanrad F, Jelinek HF. EEG-based Emotion Recognition Using Sub-Band Time-Delay Correlations. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083727 DOI: 10.1109/embc40787.2023.10340014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Emotion recognition is a challenging task with many potential applications in psychology, psychiatry, and human-computer interaction (HCI). The use of time-delay information in the controlled time-delay stability (cTDS) algorithm can help to capture the temporal dynamics of EEG signals, including sub-band information and bi-directional coupling that can aid in emotion recognition and identification of specific connectivity patterns between brain rhythms. Incorporating EEG frequency bands can be used to design better emotion recognition systems. This paper evaluates the cTDS algorithm for binary classification tasks of arousal and valence using EEG sub-band signals. This method achieved a high accuracy of 91.1% for arousal and 91.7% for valence based on one electrode recording site at Fp1. The cTDS algorithm is a promising approach to analyzing brain network interactions. It can be particularly applicable to arousal and valence classification tasks, especially within a complex, multimodal feature space associated with understanding psychiatric disorders and HCI applications.
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18
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Yen C, Lin CL, Chiang MC. Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders. Life (Basel) 2023; 13:1472. [PMID: 37511847 PMCID: PMC10381462 DOI: 10.3390/life13071472] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/12/2023] [Accepted: 06/27/2023] [Indexed: 07/30/2023] Open
Abstract
Neuroimaging has revolutionized our understanding of brain function and has become an essential tool for researchers studying neurological disorders. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two widely used neuroimaging techniques to review changes in brain activity. fMRI is a noninvasive technique that uses magnetic fields and radio waves to produce detailed brain images. An EEG is a noninvasive technique that records the brain's electrical activity through electrodes placed on the scalp. This review overviews recent developments in noninvasive functional neuroimaging methods, including fMRI and EEG. Recent advances in fMRI technology, its application to studying brain function, and the impact of neuroimaging techniques on neuroscience research are discussed. Advances in EEG technology and its applications to analyzing brain function and neural oscillations are also highlighted. In addition, advanced courses in neuroimaging, such as diffusion tensor imaging (DTI) and transcranial electrical stimulation (TES), are described, along with their role in studying brain connectivity, white matter tracts, and potential treatments for schizophrenia and chronic pain. Application. The review concludes by examining neuroimaging studies of neurodevelopmental and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease (AD), and Parkinson's disease (PD). We also described the role of transcranial direct current stimulation (tDCS) in ASD, ADHD, AD, and PD. Neuroimaging techniques have significantly advanced our understanding of brain function and provided essential insights into neurological disorders. However, further research into noninvasive treatments such as EEG, MRI, and TES is necessary to continue to develop new diagnostic and therapeutic strategies for neurological disorders.
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Affiliation(s)
- Chiahui Yen
- Department of International Business, Ming Chuan University, Taipei 111, Taiwan
| | - Chia-Li Lin
- Department of International Business, Ming Chuan University, Taipei 111, Taiwan
| | - Ming-Chang Chiang
- Department of Life Science, College of Science and Engineering, Fu Jen Catholic University, New Taipei City 242, Taiwan
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19
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Sugden RJ, Pham-Kim-Nghiem-Phu VLL, Campbell I, Leon A, Diamandis P. Remote collection of electrophysiological data with brain wearables: opportunities and challenges. Bioelectron Med 2023; 9:12. [PMID: 37340487 DOI: 10.1186/s42234-023-00114-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 05/30/2023] [Indexed: 06/22/2023] Open
Abstract
Collection of electroencephalographic (EEG) data provides an opportunity to non-invasively study human brain plasticity, learning and the evolution of various neuropsychiatric disorders. Traditionally, due to sophisticated hardware, EEG studies have been largely limited to research centers which restrict both testing contexts and repeated longitudinal measures. The emergence of low-cost "wearable" EEG devices now provides the prospect of frequent and remote monitoring of the human brain for a variety of physiological and pathological brain states. In this manuscript, we survey evidence that EEG wearables provide high-quality data and review various software used for remote data collection. We then discuss the growing body of evidence supporting the feasibility of remote and longitudinal EEG data collection using wearables including a discussion of potential biomedical applications of these protocols. Lastly, we discuss some additional challenges needed for EEG wearable research to gain further widespread adoption.
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Affiliation(s)
- Richard James Sugden
- Department of Medical Biophysics, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Princess Margaret Cancer Center, University Health Network, 610 University Avenue, Toronto, ON, M5G 2C1, Canada
| | | | - Ingrid Campbell
- Princess Margaret Cancer Center, University Health Network, 610 University Avenue, Toronto, ON, M5G 2C1, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Alberto Leon
- Princess Margaret Cancer Center, University Health Network, 610 University Avenue, Toronto, ON, M5G 2C1, Canada
| | - Phedias Diamandis
- Department of Medical Biophysics, University of Toronto, Toronto, ON, M5S 1A8, Canada.
- Laboratory Medicine Program, University Health Network, 200 Elizabeth Street, Toronto, ON, M5G 2C4, Canada.
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20
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Gelbard-Sagiv H, Pardo S, Getter N, Guendelman M, Benninger F, Kraus D, Shriki O, Ben-Sasson S. Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:5805. [PMID: 37447653 DOI: 10.3390/s23135805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/15/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023]
Abstract
Epilepsy, a prevalent neurological disorder, profoundly affects patients' quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. However, optimizing electrode configurations for such systems, which is crucial for balancing accuracy and practicality, remains to be explored. This study addresses this gap by developing a systematic approach to optimize electrode configurations for a seizure detection machine-learning algorithm. Our approach was applied to an extensive database of prolonged annotated EEG recordings from 158 epilepsy patients. Multiple electrode configurations ranging from one to eighteen were assessed to determine the optimal number of electrodes. Results indicated that the performance was initially maintained as the number of electrodes decreased, but a drop in performance was found to have occurred at around eight electrodes. Subsequently, a comprehensive analysis of all eight-electrode configurations was conducted using a computationally intensive workflow to identify the optimal configurations. This approach can inform the mechanical design process of an EEG system that balances seizure detection accuracy with the ease of use and portability. Additionally, this framework holds potential for optimizing hardware in other machine learning applications. The study presents a significant step towards the development of an efficient wearable EEG system for seizure detection.
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Affiliation(s)
| | - Snir Pardo
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
| | - Nir Getter
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Miriam Guendelman
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Felix Benninger
- Department of Neurology, Rabin Medical Center, Beilinson Hospital, Petach Tikva 4941492, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Dror Kraus
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Pediatric Neurology, Schneider Children's Medical Center of Israel, Petach Tikva 4920235, Israel
| | - Oren Shriki
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
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21
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López-Larraz E, Escolano C, Robledo-Menéndez A, Morlas L, Alda A, Minguez J. A garment that measures brain activity: proof of concept of an EEG sensor layer fully implemented with smart textiles. Front Hum Neurosci 2023; 17:1135153. [PMID: 37305362 PMCID: PMC10250743 DOI: 10.3389/fnhum.2023.1135153] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 04/20/2023] [Indexed: 06/13/2023] Open
Abstract
This paper presents the first garment capable of measuring brain activity with accuracy comparable to that of state-of-the art dry electroencephalogram (EEG) systems. The main innovation is an EEG sensor layer (i.e., the electrodes, the signal transmission, and the cap support) made entirely of threads, fabrics, and smart textiles, eliminating the need for metal or plastic materials. The garment is connected to a mobile EEG amplifier to complete the measurement system. As a first proof of concept, the new EEG system (Garment-EEG) was characterized with respect to a state-of-the-art Ag/AgCl dry-EEG system (Dry-EEG) over the forehead area of healthy participants in terms of: (1) skin-electrode impedance; (2) EEG activity; (3) artifacts; and (4) user ergonomics and comfort. The results show that the Garment-EEG system provides comparable recordings to Dry-EEG, but it is more susceptible to artifacts under adverse recording conditions due to poorer contact impedances. The textile-based sensor layer offers superior ergonomics and comfort compared to its metal-based counterpart. We provide the datasets recorded with Garment-EEG and Dry-EEG systems, making available the first open-access dataset of an EEG sensor layer built exclusively with textile materials. Achieving user acceptance is an obstacle in the field of neurotechnology. The introduction of EEG systems encapsulated in wearables has the potential to democratize neurotechnology and non-invasive brain-computer interfaces, as they are naturally accepted by people in their daily lives. Furthermore, supporting the EEG implementation in the textile industry may result in lower cost and less-polluting manufacturing processes compared to metal and plastic industries.
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22
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Yadav H, Maini S. Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-45. [PMID: 37362726 PMCID: PMC10157593 DOI: 10.1007/s11042-023-15653-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 07/17/2022] [Accepted: 04/22/2023] [Indexed: 06/28/2023]
Abstract
Brain-Computer Interfaces (BCI) is an exciting and emerging research area for researchers and scientists. It is a suitable combination of software and hardware to operate any device mentally. This review emphasizes the significant stages in the BCI domain, current problems, and state-of-the-art findings. This article also covers how current results can contribute to new knowledge about BCI, an overview of BCI from its early developments to recent advancements, BCI applications, challenges, and future directions. The authors pointed to unresolved issues and expressed how BCI is valuable for analyzing the human brain. Humans' dependence on machines has led humankind into a new future where BCI can play an essential role in improving this modern world.
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Affiliation(s)
- Hitesh Yadav
- Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering & Technology, Longowal, Punjab India
| | - Surita Maini
- Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering & Technology, Longowal, Punjab India
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23
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Haumann NT, Petersen B, Vuust P, Brattico E. Age differences in central auditory system responses to naturalistic music. Biol Psychol 2023; 179:108566. [PMID: 37086903 DOI: 10.1016/j.biopsycho.2023.108566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 04/24/2023]
Abstract
Aging influences the central auditory system leading to difficulties in the decoding and understanding of overlapping sound signals, such as speech in noise or polyphonic music. Studies on central auditory system evoked responses (ERs) have found in older compared to young listeners increased amplitudes (less inhibition) of the P1 and N1 and decreased amplitudes of the P2, mismatch negativity (MMN), and P3a responses. While preceding research has focused on simplified auditory stimuli, we here tested whether the previously observed age-related differences could be replicated with sounds embedded in medium and highly naturalistic musical contexts. Older (age 55-77 years) and younger adults (age 21-31 years) listened to medium naturalistic (synthesized melody) and highly naturalistic (studio recording of a music piece) stimuli. For the medium naturalistic music, the age group differences on the P1, N1, P2, MMN, and P3a amplitudes were all replicated. The age group differences, however, appeared reduced with the highly compared to the medium naturalistic music. The finding of lower P2 amplitude in older than young was replicated for slow event rates (0.3-2.9Hz) in the highly naturalistic music. Moreover, the ER latencies suggested a gradual slowing of the auditory processing time course for highly compared to medium naturalistic stimuli irrespective of age. These results support that age-related differences on ERs can partly be observed with naturalistic stimuli. This opens new avenues for including naturalistic stimuli in the investigation of age-related central auditory system disorders.
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Affiliation(s)
- Niels Trusbak Haumann
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus/Aalborg, Universitetsbyen 3, 8000 Aarhus C, Denmark.
| | - Bjørn Petersen
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus/Aalborg, Universitetsbyen 3, 8000 Aarhus C, Denmark
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus/Aalborg, Universitetsbyen 3, 8000 Aarhus C, Denmark
| | - Elvira Brattico
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus/Aalborg, Universitetsbyen 3, 8000 Aarhus C, Denmark
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24
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Wireless EEG: A survey of systems and studies. Neuroimage 2023; 269:119774. [PMID: 36566924 DOI: 10.1016/j.neuroimage.2022.119774] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 11/18/2022] [Accepted: 11/27/2022] [Indexed: 12/24/2022] Open
Abstract
The popular brain monitoring method of electroencephalography (EEG) has seen a surge in commercial attention in recent years, focusing mostly on hardware miniaturization. This has led to a varied landscape of portable EEG devices with wireless capability, allowing them to be used by relatively unconstrained users in real-life conditions outside of the laboratory. The wide availability and relative affordability of these devices provide a low entry threshold for newcomers to the field of EEG research. The large device variety and the at times opaque communication from their manufacturers, however, can make it difficult to obtain an overview of this hardware landscape. Similarly, given the breadth of existing (wireless) EEG knowledge and research, it can be challenging to get started with novel ideas. Therefore, this paper first provides a list of 48 wireless EEG devices along with a number of important-sometimes difficult-to-obtain-features and characteristics to enable their side-by-side comparison, along with a brief introduction to each of these aspects and how they may influence one's decision. Secondly, we have surveyed previous literature and focused on 110 high-impact journal publications making use of wireless EEG, which we categorized by application and analyzed for device used, number of channels, sample size, and participant mobility. Together, these provide a basis for informed decision making with respect to hardware and experimental precedents when considering new, wireless EEG devices and research. At the same time, this paper provides background material and commentary about pitfalls and caveats regarding this increasingly accessible line of research.
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Saibene A, Caglioni M, Corchs S, Gasparini F. EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:2798. [PMID: 36905004 PMCID: PMC10007053 DOI: 10.3390/s23052798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/21/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain-computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.
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Affiliation(s)
- Aurora Saibene
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
- NeuroMI, Milan Center for Neuroscience, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
| | - Mirko Caglioni
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
| | - Silvia Corchs
- NeuroMI, Milan Center for Neuroscience, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
- Department of Theoretical and Applied Sciences, University of Insubria, Via J. H. Dunant 3, 21100 Varese, Italy
| | - Francesca Gasparini
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
- NeuroMI, Milan Center for Neuroscience, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
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26
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Dan J, Foged MT, Vandendriessche B, Van Paesschen W, Bertrand A. Sensor selection and miniaturization limits for detection of interictal epileptiform discharges with wearable EEG. J Neural Eng 2023; 20. [PMID: 36630712 DOI: 10.1088/1741-2552/acb231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 01/11/2023] [Indexed: 01/12/2023]
Abstract
Objective.The goal of this paper is to investigate the limits of electroencephalography (EEG) sensor miniaturization in a set-up consisting of multiple galvanically isolated EEG units to record interictal epileptiform discharges (IEDs), referred to as 'spikes', in people with epilepsy.Approach.A dataset of high-density EEG recordings (257 channels) was used to emulate local EEG sensor units with short inter-electrode distances. A computationally efficient sensor selection and interictal spike detection algorithm was developed and used to assess the influence of the inter-electrode distance and the number of such EEG units on spike detection performance. Signal-to-noise ratio, correlation with a clinical-grade IEDs detector and Cohen's kappa coefficient of agreement were used to quantify performance. Bayesian statistics were used to confirm the statistical significance of the observed results.Main results.We found that EEG recording equipment should be specifically designed to measure the small signal power at short inter-electrode distance by providing an input referred noise<300 nV. We also found that an inter-electrode distance of minimum 5 cm between electrodes in a setup with a minimum of two EEG units is required to obtain near equivalent performance in interictal spike detection to standard EEG.Significance.These findings provide design guidelines for miniaturizing EEG systems for long term ambulatory monitoring of interictal spikes in epilepsy patients.
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Affiliation(s)
- Jonathan Dan
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium.,Byteflies, Borsbeeksebrug 22, 2600 Berchem, Belgium
| | - Mette Thrane Foged
- Rigshospitalet, Neurobiology Research Unit, 28 Juliane Maries Vej, DK-2100 Copenhagen, Denmark
| | - Benjamin Vandendriessche
- Byteflies, Borsbeeksebrug 22, 2600 Berchem, Belgium.,Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Wim Van Paesschen
- Department of neurology, UZ Leuven, Herestraat 49, 3000 Leuven, Belgium
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Penner F, Wall KM, Guan KW, Huang HJ, Richardson L, Dunbar AS, Groh AM, Rutherford HJV. Racial disparities in EEG research and their implications for our understanding of the maternal brain. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023; 23:1-16. [PMID: 36414837 PMCID: PMC9684773 DOI: 10.3758/s13415-022-01040-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 09/26/2022] [Indexed: 11/24/2022]
Abstract
Racial disparities in maternal health are alarming and persistent. Use of electroencephalography (EEG) and event-related potentials (ERPs) to understand the maternal brain can improve our knowledge of maternal health by providing insight into mechanisms underlying maternal well-being, including implications for child development. However, systematic racial bias exists in EEG methodology-particularly for Black individuals-and in psychological and health research broadly. This paper discusses these biases in the context of EEG/ERP research on the maternal brain. First, we assess the racial/ethnic diversity of existing ERP studies of maternal neural responding to infant/child emotional expressions, using papers from a recent meta-analysis, finding that the majority of mothers represented in this research are of White/European ancestry and that the racially and ethnically diverse samples that are present are limited in terms of geography. Therefore, our current knowledge base in this area may be biased and not generalizable across racially diverse mothers. We outline factors underlying this problem, beginning with the racial bias in EEG equipment that systematically excludes individuals of African descent, and also considering factors specific to research with mothers. Finally, we highlight recent innovations to EEG hardware to better accommodate diverse hairstyles and textures, and other important steps to increase racial and ethnic representativeness in EEG/ERP research with mothers. We urge EEG/ERP researchers who study the maternal brain-including our own research group-to take action to increase racial diversity so that this research area can confidently inform understanding of maternal health and contribute to minimizing maternal health disparities.
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Affiliation(s)
| | - Kathryn M Wall
- Yale Child Study Center, Yale University, New Haven, CT, USA
| | - Kathleen W Guan
- Yale Child Study Center, Yale University, New Haven, CT, USA
| | - Helen J Huang
- Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, USA
| | - Lietsel Richardson
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL, USA
| | - Angel S Dunbar
- Department of African American Studies, University of Maryland, College Park, MD, USA
| | - Ashley M Groh
- Department of Psychological Sciences, University of Missouri, Columbia, MO, USA
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28
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Abdel-Hamid L. An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031255. [PMID: 36772295 PMCID: PMC9921881 DOI: 10.3390/s23031255] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 01/14/2023] [Accepted: 01/17/2023] [Indexed: 05/17/2023]
Abstract
Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone of speech have been previously considered for emotion recognition, yet they have the drawback of being easily manipulated by subjects to mask their true emotions. Electroencephalography (EEG) has emerged as a reliable and cost-effective method to detect true human emotions. Recently, huge research effort has been put to develop efficient wearable EEG devices to be used by consumers in out of the lab scenarios. In this work, a subject-dependent emotional valence recognition method is implemented that is intended for utilization in emotion AI applications. Time and frequency features were computed from a single time series derived from the Fp1 and Fp2 channels. Several analyses were performed on the strongest valence emotions to determine the most relevant features, frequency bands, and EEG timeslots using the benchmark DEAP dataset. Binary classification experiments resulted in an accuracy of 97.42% using the alpha band, by that outperforming several approaches from literature by ~3-22%. Multiclass classification gave an accuracy of 95.0%. Feature computation and classification required less than 0.1 s. The proposed method thus has the advantage of reduced computational complexity as, unlike most methods in the literature, only two EEG channels were considered. In addition, minimal features concluded from the thorough analyses conducted in this study were used to achieve state-of-the-art performance. The implemented EEG emotion recognition method thus has the merits of being reliable and easily reproducible, making it well-suited for wearable EEG devices.
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Affiliation(s)
- Lamiaa Abdel-Hamid
- Department of Electronics & Communication, Faculty of Engineering, Misr International University (MIU), Heliopolis, Cairo P.O. Box 1 , Egypt
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29
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Yarici MC, Thornton M, Mandic DP. Ear-EEG sensitivity modeling for neural sources and ocular artifacts. Front Neurosci 2023; 16:997377. [PMID: 36699519 PMCID: PMC9868963 DOI: 10.3389/fnins.2022.997377] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 12/09/2022] [Indexed: 01/12/2023] Open
Abstract
The ear-EEG has emerged as a promising candidate for real-world wearable brain monitoring. While experimental studies have validated several applications of ear-EEG, the source-sensor relationship for neural sources from across the brain surface has not yet been established. In addition, modeling of the ear-EEG sensitivity to sources of artifacts is still missing. Through volume conductor modeling, the sensitivity of various configurations of ear-EEG is established for a range of neural sources, in addition to ocular artifact sources for the blink, vertical saccade, and horizontal saccade eye movements. Results conclusively support the introduction of ear-EEG into conventional EEG paradigms for monitoring neural activity that originates from within the temporal lobes, while also revealing the extent to which ear-EEG can be used for sources further away from these regions. The use of ear-EEG in scenarios prone to ocular artifacts is also supported, through the demonstration of proportional scaling of artifacts and neural signals in various configurations of ear-EEG. The results from this study can be used to support both existing and prospective experimental ear-EEG studies and applications in the context of sensitivity to both neural sources and ocular artifacts.
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30
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Mascia A, Collu R, Spanu A, Fraschini M, Barbaro M, Cosseddu P. Wearable System Based on Ultra-Thin Parylene C Tattoo Electrodes for EEG Recording. SENSORS (BASEL, SWITZERLAND) 2023; 23:766. [PMID: 36679563 PMCID: PMC9861766 DOI: 10.3390/s23020766] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/03/2023] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
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 tattoo electrodes. The proposed system has been tested in a standard rest-state experiment, and its performance in terms of discrimination of two different states has been compared to that of a commercial wearable device for EEG signal acquisition (i.e., the Muse headset), showing comparable results. This first preliminary validation demonstrates the possibility of conveniently employing ultra-conformable tattoo-electrodes integrated portable systems for the unobtrusive acquisition of brain activity.
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Affiliation(s)
- Antonello Mascia
- Department of Electrical and Electronics Engineering, University of Cagliari, Piazza D’Armi, 09123 Cagliari, Italy
| | - Riccardo Collu
- Department of Electrical and Electronics Engineering, University of Cagliari, Piazza D’Armi, 09123 Cagliari, Italy
| | - Andrea Spanu
- Department of Science, Technology and Society, Scuola Universitaria Superiore IUSS, Palazzo del Broletto, Piazza della Vittoria 15, 27100 Pavia, Italy
| | - Matteo Fraschini
- Department of Electrical and Electronics Engineering, University of Cagliari, Piazza D’Armi, 09123 Cagliari, Italy
| | - Massimo Barbaro
- Department of Electrical and Electronics Engineering, University of Cagliari, Piazza D’Armi, 09123 Cagliari, Italy
| | - Piero Cosseddu
- Department of Electrical and Electronics Engineering, University of Cagliari, Piazza D’Armi, 09123 Cagliari, Italy
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31
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Al-Daraghmeh MY, Stone RT. A review of medical wearables: materials, power sources, sensors, and manufacturing aspects of human wearable technologies. J Med Eng Technol 2023; 47:67-81. [PMID: 35856912 DOI: 10.1080/03091902.2022.2097743] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Wearable technology is a promising and revolutionary technology that is changing some aspects of our standard of living to a great extent, including health monitoring, sport and fitness, performance tracking, education, and entertainment. This article presents a comprehensive literature review of over 160 articles related to state-of-the-art human wearable technologies. We provide a thorough understanding of the materials, power sources, sensors, and manufacturing processes, and the relationships between these to capture opportunities for enhancement and challenges to overcome in wearables. As a result of our review, we have determined the need for the development of a comprehensive, robust manufacturing system alongside specific standards and regulations that take into account wearables' unique characteristics. Seeing the whole picture will provide a frame reference and road map for researchers and industries through the design, manufacturing, and commercialisation of effective, portable, self-powered, multi-sensing ultimate future wearable devices and create opportunities for new innovations and applications.
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Affiliation(s)
- Mohammad Y Al-Daraghmeh
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, USA.,Department of Industrial Engineering, Yarmouk University, Irbid, Jordan
| | - Richard T Stone
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, USA
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Del Percio C, Lopez S, Noce G, Lizio R, Tucci F, Soricelli A, Ferri R, Nobili F, Arnaldi D, Famà F, Buttinelli C, Giubilei F, Marizzoni M, Güntekin B, Yener G, Stocchi F, Vacca L, Frisoni GB, Babiloni C. What a Single Electroencephalographic (EEG) Channel Can Tell us About Alzheimer's Disease Patients With Mild Cognitive Impairment. Clin EEG Neurosci 2023; 54:21-35. [PMID: 36413420 DOI: 10.1177/15500594221125033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abnormalities in cortical sources of resting-state eyes closed electroencephalographic (rsEEG) rhythms recorded by hospital settings (10-20 montage) with 19 scalp electrodes characterized Alzheimer's disease (AD) from preclinical to dementia stages. An intriguing rsEEG application is the monitoring and evaluation of AD progression in large populations with few electrodes in low-cost devices. Here we evaluated whether the above-mentioned abnormalities can be observed from fewer scalp electrodes in patients with mild cognitive impairment due to AD (ADMCI). Clinical and rsEEG data acquired in hospital settings (10-20 montage) from 75 ADMCI participants and 70 age-, education-, and sex-matched normal elderly controls (Nold) were available in an Italian-Turkish archive (PDWAVES Consortium; www.pdwaves.eu). Standard spectral fast fourier transform (FFT) analysis of rsEEG data for individual delta, theta, and alpha frequency bands was computed from 6 monopolar scalp electrodes to derive bipolar C3-P3, C4-P4, P3-O1, and P4-O2 markers. The ADMCI group showed increased delta and decreased alpha power density at the C3-P3, C4-P4, P3-O1, and P4-O2 bipolar channels compared to the Nold group. Increased theta power density for ADMCI patients was observed only at the C3-P3 bipolar channel. Best classification accuracy between the ADMCI and Nold individuals reached 81% (area under the receiver operating characteristic curve) using Alpha2/Theta power density computed at the C3-P3 bipolar channel. Standard rsEEG power density computed from six posterior bipolar channels characterized ADMCI status. These results may pave the way toward diffuse clinical applications in health monitoring of dementia using low-cost EEG systems with a strict number of electrodes in lower- and middle-income countries.
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Affiliation(s)
- Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy
| | | | | | - Federico Tucci
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy
| | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy.,Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | | | - Flavio Nobili
- Clinica neurologica, 9246IRCCS Ospedale Policlinico San Martino, Genova, Italy.,Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), 27212Università di Genova, Italy
| | - Dario Arnaldi
- Clinica neurologica, 9246IRCCS Ospedale Policlinico San Martino, Genova, Italy.,Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), 27212Università di Genova, Italy
| | - Francesco Famà
- Clinica neurologica, 9246IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, 9311Sapienza University of Rome, Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, 9311Sapienza University of Rome, Rome, Italy
| | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, 218502Istanbul Medipol University, Istanbul, Turkey.,REMER, Clinical Electrophysiology, Neuroimaging and Neuromodulation Lab., 218502Istanbul Medipol University, Istanbul, Turkey
| | - Görsev Yener
- Izmir University of Economics, Faculty of Medicine, Izmir, Turkey
| | | | | | - Giovanni B Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and 27212University of Geneva, Geneva, Switzerland
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy.,Hospital San Raffaele Cassino, Cassino (FR), Italy
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What a single electroencephalographic (EEG) channel can tell us about patients with dementia due to Alzheimer's disease. Int J Psychophysiol 2022; 182:169-181. [DOI: 10.1016/j.ijpsycho.2022.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022]
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Lee K, Choi KM, Park S, Lee SH, Im CH. Selection of the optimal channel configuration for implementing wearable EEG devices for the diagnosis of mild cognitive impairment. Alzheimers Res Ther 2022; 14:170. [DOI: 10.1186/s13195-022-01115-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/31/2022] [Indexed: 11/15/2022]
Abstract
Abstract
Background
Early diagnosis of mild cognitive impairment (MCI) is essential for timely treatment planning. With recent advances in the wearable technology, interest has increasingly shifted toward computer-aided self-diagnosis of MCI using wearable electroencephalography (EEG) devices in daily life. However, no study so far has investigated the optimal electrode configurations for the efficient diagnosis of MCI while considering the design factors of wearable EEG devices. In this study, we aimed to determine the optimal channel configurations of wearable EEG devices for the computer-aided diagnosis of MCI.
Method
We employed an EEG dataset collected from 21 patients with MCI and 21 healthy control subjects. After evaluating the classification accuracies for all possible electrode configurations for the two-, four-, six-, and eight-electrode conditions using a support vector machine, the optimal electrode configurations that provide the highest diagnostic accuracy were suggested for each electrode condition.
Results
The highest classification accuracies of 74.04% ± 4.82, 82.43% ± 6.14, 86.28% ± 2.81, and 86.85% ± 4.97 were achieved for the optimal two-, four-, six-, and eight-electrode configurations, respectively, which demonstrated the possibility of precise machine-learning-based diagnosis of MCI with a limited number of EEG electrodes. Additionally, further simulations with the EEG dataset revealed that the optimal electrode configurations had significantly higher classification accuracies than commercial EEG devices with the same number of electrodes, which suggested the importance of electrode configuration optimization for wearable EEG devices based on clinical EEG datasets.
Conclusions
This study highlighted that the optimization of the electrode configuration, assuming the wearable EEG devices can potentially be utilized for daily life monitoring of MCI, is necessary to enhance the performance and portability.
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Anders C, Arnrich B. Wearable electroencephalography and multi-modal mental state classification: A systematic literature review. Comput Biol Med 2022; 150:106088. [PMID: 36137314 DOI: 10.1016/j.compbiomed.2022.106088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/10/2022] [Accepted: 09/03/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Wearable multi-modal time-series classification applications outperform their best uni-modal counterparts and hold great promise. A modality that directly measures electrical correlates from the brain is electroencephalography. Due to varying noise sources, different key brain regions, key frequency bands, and signal characteristics like non-stationarity, techniques for data pre-processing and classification algorithms are task-dependent. METHOD Here, a systematic literature review on mental state classification for wearable electroencephalography is presented. Four search terms in different combinations were used for an in-title search. The search was executed on the 29th of June 2022, across Google Scholar, PubMed, IEEEXplore, and ScienceDirect. 76 most relevant publications were set into context as the current state-of-the-art in mental state time-series classification. RESULTS Pre-processing techniques, features, and time-series classification models were analyzed. Across publications, a window length of one second was mainly chosen for classification and spectral features were utilized the most. The achieved performance per time-series classification model is analyzed, finding linear discriminant analysis, decision trees, and k-nearest neighbors models outperform support-vector machines by a factor of up to 1.5. A historical analysis depicts future trends while under-reported aspects relevant to practical applications are discussed. CONCLUSIONS Five main conclusions are given, covering utilization of available area for electrode placement on the head, most often or scarcely utilized features and time-series classification model architectures, baseline reporting practices, as well as explainability and interpretability of Deep Learning. The importance of a 'test battery' assessing the influence of data pre-processing and multi-modality on time-series classification performance is emphasized.
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Affiliation(s)
- Christoph Anders
- Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Brandenburg, Germany.
| | - Bert Arnrich
- Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Brandenburg, Germany.
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36
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Cui X, Wu Y, Wu J, You Z, Xiahou J, Ouyang M. A review: Music-emotion recognition and analysis based on EEG signals. Front Neuroinform 2022; 16:997282. [PMID: 36387584 PMCID: PMC9640432 DOI: 10.3389/fninf.2022.997282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/11/2022] [Indexed: 11/25/2022] Open
Abstract
Music plays an essential role in human life and can act as an expression to evoke human emotions. The diversity of music makes the listener's experience of music appear diverse. Different music can induce various emotions, and the same theme can also generate other feelings related to the listener's current psychological state. Music emotion recognition (MER) has recently attracted widespread attention in academics and industry. With the development of brain science, MER has been widely used in different fields, e.g., recommendation systems, automatic music composing, psychotherapy, and music visualization. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. Besides, electroencephalography (EEG) enables external devices to sense neurophysiological signals in the brain without surgery. This non-invasive brain-computer signal has been used to explore emotions. This paper surveys EEG music emotional analysis, involving the analysis process focused on the music emotion analysis method, e.g., data processing, emotion model, and feature extraction. Then, challenging problems and development trends of EEG-based music emotion recognition is proposed. Finally, the whole paper is summarized.
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Affiliation(s)
- Xu Cui
- The Art School, Xiamen University, Xiamen, China
| | - Yongrong Wu
- Department of Software Engineering, Xiamen University, Xiamen, China
| | - Jipeng Wu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhiyu You
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, China
| | - Jianbing Xiahou
- The Mathematics and Computer School, Quanzhou Normal University, Quanzhou, China
| | - Menglin Ouyang
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo, China
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Bourguignon NJ, Bue SL, Guerrero-Mosquera C, Borragán G. Bimodal EEG-fNIRS in Neuroergonomics. Current Evidence and Prospects for Future Research. FRONTIERS IN NEUROERGONOMICS 2022; 3:934234. [PMID: 38235461 PMCID: PMC10790898 DOI: 10.3389/fnrgo.2022.934234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/20/2022] [Indexed: 01/19/2024]
Abstract
Neuroergonomics focuses on the brain signatures and associated mental states underlying behavior to design human-machine interfaces enhancing performance in the cognitive and physical domains. Brain imaging techniques such as functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) have been considered key methods for achieving this goal. Recent research stresses the value of combining EEG and fNIRS in improving these interface systems' mental state decoding abilities, but little is known about whether these improvements generalize over different paradigms and methodologies, nor about the potentialities for using these systems in the real world. We review 33 studies comparing mental state decoding accuracy between bimodal EEG-fNIRS and unimodal EEG and fNIRS in several subdomains of neuroergonomics. In light of these studies, we also consider the challenges of exploiting wearable versions of these systems in real-world contexts. Overall the studies reviewed suggest that bimodal EEG-fNIRS outperforms unimodal EEG or fNIRS despite major differences in their conceptual and methodological aspects. Much work however remains to be done to reach practical applications of bimodal EEG-fNIRS in naturalistic conditions. We consider these points to identify aspects of bimodal EEG-fNIRS research in which progress is expected or desired.
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Affiliation(s)
| | - Salvatore Lo Bue
- Department of Life Sciences, Royal Military Academy of Belgium, Brussels, Belgium
| | | | - Guillermo Borragán
- Center for Research in Cognition and Neuroscience, Université Libre de Bruxelles, Brussels, Belgium
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Casson AJ. EEG goes home. Clin Neurophysiol 2022; 142:254-255. [DOI: 10.1016/j.clinph.2022.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 08/01/2022] [Indexed: 11/03/2022]
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Automatic sleep stage classification: From classical machine learning methods to deep learning. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103751] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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40
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Mean curve length: An efficient feature for brainwave biometrics. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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41
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Water-soluble adhesive for stable long-term ambulatory EEG recordings. Clin Neurophysiol 2022; 142:258-261. [DOI: 10.1016/j.clinph.2022.07.490] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/07/2022] [Accepted: 07/12/2022] [Indexed: 11/21/2022]
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Kappel SL, Kidmose P. Characterization of Dry-Contact EEG Electrodes and an Empirical Comparison of Ag/AgCl and IrO 2 Electrodes. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3127-3130. [PMID: 36086317 DOI: 10.1109/embc48229.2022.9871923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Dry-contact electrodes are increasingly being used for EEG recordings in both research studies and consumer products. They are more user-friendly and better suited for long-term recordings. However, dry-contact electrodes also bring challenges with respect to the stability and impedance of the electrode-skin interface. We propose a methodology to characterize and compare dry-contact electrodes. The characterization is based on measuring the electrode-skin impedance spectrum, fit a parametric model of the electrode-skin interface to the measured spectrum, and calculate the resulting thermal noise spectrum. Thereby it is possible to relate the noise of an EEG recording to the theoretical noise contribution from the electrode-skin interface. To demonstrate the methodology, we performed an empirical study comparing two types of dry-contact electrodes in an ear-EEG setup. The electrodes were IrO2, previously used for ear-EEG, and a new design based on Ag/AgCl. Here, we related the noise floor of an auditory steady-state response (ASSR) to the thermal noise spectrum of the electrode-skin interface. The study showed similar impedance and EEG recording quality for the two electrode types, and the thermal noise of the electrode-skin interface was below the noise floor of the EEG recordings for both electrode types. Dry-contact EEG is an enabling technology for long-term brain monitoring of patients. This may be relevant for example for monitoring of neurodegenerative diseases, stroke patients, patients with traumatic brain injuries, and psychiatric patients.
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Das A, Basu S, A A, Gubbi J, Muralidharan K, S M, S M, Biradar A, Pradhan U, Chakravarty T, Ramakrishnan RK, Pal A. Surface Potential Simulation and Electrode Design for in-Ear EEG Measurement. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:937-940. [PMID: 36086437 DOI: 10.1109/embc48229.2022.9871926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The need for everyday-real-time EEG sensing has resulted in the development of minimalistic and discreet wearable EEG measuring devices. A front runner in this race is in-ear worn device. However, the position of the ear as well as its restricted accessibility poses certain challenges in the design of such devices - from the type of materials used to the placement of electrodes. These choices are important as they will determine the quality of the EEG signal available and in turn the accuracy of the application. We therefore create a simulation model of the human ear, allowing us to understand the impact of these choices on our design of an In-Ear EEG wearable. We first study the signal acquisition characteristics of a proposed gold-plated electrode against two other state-of-the-art electrode materials for in-ear EEG data collection. We further validate this electrode choice by fabricating a personalized silicone-based earpiece and collecting in-situ EEG data. This work explores the properties of using gold plated electrodes in capturing in-ear EEG signals enabling unobtrusive collection of the brain physiology data in real world setting.
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Hsieh JC, Li Y, Wang H, Perz M, Tang Q, Tang KWK, Pyatnitskiy I, Reyes R, Ding H, Wang H. Design of hydrogel-based wearable EEG electrodes for medical applications. J Mater Chem B 2022; 10:7260-7280. [PMID: 35678148 DOI: 10.1039/d2tb00618a] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The electroencephalogram (EEG) is considered to be a promising method for studying brain disorders. Because of its non-invasive nature, subjects take a lower risk compared to some other invasive methods, while the systems record the brain signal. With the technological advancement of neural and material engineering, we are in the process of achieving continuous monitoring of neural activity through wearable EEG. In this article, we first give a brief introduction to EEG bands, circuits, wired/wireless EEG systems, and analysis algorithms. Then, we review the most recent advances in the interfaces used for EEG recordings, focusing on hydrogel-based EEG electrodes. Specifically, the advances for important figures of merit for EEG electrodes are reviewed. Finally, we summarize the potential medical application of wearable EEG systems.
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Affiliation(s)
- Ju-Chun Hsieh
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Yang Li
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Québec H3C3J7, Canada
| | - Huiqian Wang
- Department of Mathematics, The University of Texas at Austin, Austin, TX 78712, USA
| | - Matt Perz
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Qiong Tang
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA
| | - Kai Wing Kevin Tang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Ilya Pyatnitskiy
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Raymond Reyes
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Hong Ding
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Huiliang Wang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
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Zhao C, Li J, Guo Y. SleepContextNet: A temporal context network for automatic sleep staging based single-channel EEG. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 220:106806. [PMID: 35461126 DOI: 10.1016/j.cmpb.2022.106806] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 04/07/2022] [Accepted: 04/07/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Single-channel EEG is the most popular choice of sensing modality in sleep staging studies, because it widely conforms to the sleep staging guidelines. The current deep learning method using single-channel EEG signals for sleep staging mainly extracts the features of its surrounding epochs to obtain the short-term temporal context information of EEG epochs, and ignore the influence of the long-term temporal context information on sleep staging. However, the long-term context information includes sleep stage transition rules in a sleep cycle, which can further improve the performance of sleep staging. The aim of this research is to develop a temporal context network to capture the long-term context between EEG sleep stages. METHODS In this paper, we design a sleep staging network named SleepContextNet for sleep stage sequence. SleepContextNet can extract and utilize the long-term temporal context between consecutive EEG epochs, and combine it with the short-term context. we utilize Convolutional Neural Network(CNN) layers for learning representative features from each sleep stage and the representation features sequence learned are fed into a Recurrent Neural Network(RNN) layer for learning long-term and short-term context information among sleep stage in chronological order. In addition, we design a data augmentation algorithm for EEG to retain the long-term context information without changing the number of samples. RESULTS We evaluate the performance of our proposed network using four public datasets, the 2013 version of Sleep-EDF (SEDF), the 2018 version of Sleep-EDF Expanded (SEDFX), Sleep Heart Health Study (SHHS) and the CAP Sleep Database. The experimental results demonstrate that SleepContextNet outperforms state-of-the-art techniques in terms of different evaluation metrics by capturing long-term and short-term temporal context information. On average, accuracy of 84.8% in SEDF, 82.7% in SEDFX, 86.4% in SHHS and 78.8% in CAP are obtained under subject-independent cross validation. CONCLUSIONS The network extracts the long-term and short-term temporal context information of sleep stages from the sequence features, which utilizes the temporal dependencies among the EEG epochs effectively and improves the accuracy of sleep stages. The sleep staging method based on forward temporal context information is suitable for real-time family sleep monitoring system.
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Affiliation(s)
- Caihong Zhao
- School of Electronic and Engineer, Heilongjiang University, Harbin, 150080, China; School of Computer Science and Technology, Heilongjiang University, Harbin, 150080, China
| | - Jinbao Li
- Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China.
| | - Yahong Guo
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China.
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Kim H, Kim E, Choi C, Yeo WH. Advances in Soft and Dry Electrodes for Wearable Health Monitoring Devices. MICROMACHINES 2022; 13:mi13040629. [PMID: 35457934 PMCID: PMC9029742 DOI: 10.3390/mi13040629] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/12/2022] [Accepted: 04/13/2022] [Indexed: 01/20/2023]
Abstract
Electrophysiology signals are crucial health status indicators as they are related to all human activities. Current demands for mobile healthcare have driven considerable interest in developing skin-mounted electrodes for health monitoring. Silver-Silver chloride-based (Ag-/AgCl) wet electrodes, commonly used in conventional clinical practice, provide excellent signal quality, but cannot monitor long-term signals due to gel evaporation and skin irritation. Therefore, the focus has shifted to developing dry electrodes that can operate without gels and extra adhesives. Compared to conventional wet electrodes, dry ones offer various advantages in terms of ease of use, long-term stability, and biocompatibility. This review outlines a systematic summary of the latest research on high-performance soft and dry electrodes. In addition, we summarize recent developments in soft materials, biocompatible materials, manufacturing methods, strategies to promote physical adhesion, methods for higher breathability, and their applications in wearable biomedical devices. Finally, we discuss the developmental challenges and advantages of various dry electrodes, while suggesting research directions for future studies.
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Affiliation(s)
- Hyeonseok Kim
- Georgia Institute of Technology, George W. Woodruff School of Mechanical Engineering, Atlanta, GA 30332, USA; (H.K.); (E.K.); (C.C.)
- IEN Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Eugene Kim
- Georgia Institute of Technology, George W. Woodruff School of Mechanical Engineering, Atlanta, GA 30332, USA; (H.K.); (E.K.); (C.C.)
| | - Chanyeong Choi
- Georgia Institute of Technology, George W. Woodruff School of Mechanical Engineering, Atlanta, GA 30332, USA; (H.K.); (E.K.); (C.C.)
| | - Woon-Hong Yeo
- Georgia Institute of Technology, George W. Woodruff School of Mechanical Engineering, Atlanta, GA 30332, USA; (H.K.); (E.K.); (C.C.)
- IEN Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Neural Engineering Center, Institute for Materials, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Correspondence: ; Tel.: +1-404-385-5710
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Long-Term Polygraphic Monitoring through MEMS and Charge Transfer for Low-Power Wearable Applications. SENSORS 2022; 22:s22072566. [PMID: 35408181 PMCID: PMC9002930 DOI: 10.3390/s22072566] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/14/2022] [Accepted: 03/22/2022] [Indexed: 02/01/2023]
Abstract
In this work, we propose a wireless wearable system for the acquisition of multiple biopotentials through charge transfer electrostatic sensors realized in MEMS technology. The system is designed for low power consumption and low invasiveness, and thus candidates for long-time monitoring in free-living conditions, with data recording on an SD or wireless transmission to an external elaborator. Thanks to the wide horizon of applications, research is very active in this field, and in the last few years, some devices have been introduced on the market. The main problem with those devices is that their operation is time-limited, so they do not match the growing demand for long monitoring, which is a must-have feature in diagnosing specific diseases. Furthermore, their versatility is hampered by the fact that they have been designed to record just one type of signal. Using ST-Qvar sensors, we acquired an electrocardiogram trace and single-channel scalp electroencephalogram from the frontal lobes, together with an electrooculogram. Excellent results from all three types of acquisition tests were obtained. The power consumption is very low, demonstrating that, thanks to the MEMS technology, a continuous acquisition is feasible for several days.
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Ferster ML, Da Poian G, Menachery K, Schreiner SJ, Lustenberger C, Maric A, Huber R, Baumann CR, Karlen W. Benchmarking real-time algorithms for in-phase auditory stimulation of low amplitude slow waves with wearable EEG devices during sleep. IEEE Trans Biomed Eng 2022; 69:2916-2925. [PMID: 35259094 DOI: 10.1109/tbme.2022.3157468] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Auditory stimulation of EEG slow waves (SW) during non-rapid eye movement (NREM) sleep has shown to improve cognitive function when it is delivered at the up-phase of SW. SW enhancement is particularly desirable in subjects with low-amplitude SW such as older adults or patients suffering from neurodegeneration such as Parkinson disease (PD). However, existing algorithms to estimate the up-phase suffer from a poor phase accuracy at low EEG amplitudes and when SW frequencies are not constant. We introduce two novel algorithms for real-time EEG phase estimation on autonomous wearable devices. The algorithms were based on a phase-locked loop (PLL) and, for the first time, a phase vocoder (PV). We compared these phase tracking algorithms with a simple amplitude threshold approach. The optimized algorithms were benchmarked for phase accuracy, the capacity to estimate phase at SW amplitudes between 20 and 60 V, and SW frequencies above 1 Hz on 324 recordings from healthy older adults and PD patients. Furthermore, the algorithms were implemented on a wearable device and the computational efficiency and the performance was evaluated on simulated sleep EEG, as well as prospectively during a recording with a PD patient. All three algorithms delivered more than 70% of the stimulation triggers during the SW up-phase. The PV showed the highest capacity on targeting low-amplitude SW and SW with frequencies above 1 Hz. The testing on real-time hardware revealed that both PV and PLL have marginal impact on microcontroller load, while the efficiency of the PV was 4% lower than the PLL. Active auditory stimulation did not influence the phase tracking. This work demonstrated that phase-accurate auditory stimulation can be delivered during home-based sleep interventions with a wearable device also in populations with low-amplitude SW.
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Signal Quality Investigation of a New Wearable Frontal Lobe EEG Device. SENSORS 2022; 22:s22051898. [PMID: 35271044 PMCID: PMC8914983 DOI: 10.3390/s22051898] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 02/24/2022] [Accepted: 02/26/2022] [Indexed: 02/04/2023]
Abstract
The demand for non-laboratory and long-term EEG acquisition in scientific and clinical applications has put forward new requirements for wearable EEG devices. In this paper, a new wearable frontal EEG device called Mindeep was proposed. A signal quality study was then conducted, which included simulated signal tests and signal quality comparison experiments. Simulated signals with different frequencies and amplitudes were used to test the stability of Mindeep’s circuit, and the high correlation coefficients (>0.9) proved that Mindeep has a stable and reliable hardware circuit. The signal quality comparison experiment, between Mindeep and the gold standard device, Neuroscan, included three tasks: (1) resting; (2) auditory oddball; and (3) attention. In the resting state, the average normalized cross-correlation coefficients between EEG signals recorded by the two devices was around 0.72 ± 0.02, Berger effect was observed (p < 0.01), and the comparison results in the time and frequency domain illustrated the ability of Mindeep to record high-quality EEG signals. The significant differences between high tone and low tone in auditory event-related potential collected by Mindeep was observed in N2 and P2. The attention recognition accuracy of Mindeep achieved 71.12% and 74.76% based on EEG features and the XGBoost model in the two attention tasks, respectively, which were higher than that of Neuroscan (70.19% and 72.80%). The results validated the performance of Mindeep as a prefrontal EEG recording device, which has a wide range of potential applications in audiology, cognitive neuroscience, and daily requirements.
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Ha J, Park S, Im CH. Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain-Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate. Front Neuroinform 2022; 16:758537. [PMID: 35281718 PMCID: PMC8908008 DOI: 10.3389/fninf.2022.758537] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 01/27/2022] [Indexed: 11/13/2022] Open
Abstract
Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have recently attracted increasing attention in virtual reality (VR) applications as a promising tool for controlling virtual objects or generating commands in a "hands-free" manner. Video-oculography (VOG) has been frequently used as a tool to improve BCI performance by identifying the gaze location on the screen, however, current VOG devices are generally too expensive to be embedded in practical low-cost VR head-mounted display (HMD) systems. In this study, we proposed a novel calibration-free hybrid BCI system combining steady-state visual-evoked potential (SSVEP)-based BCI and electrooculogram (EOG)-based eye tracking to increase the information transfer rate (ITR) of a nine-target SSVEP-based BCI in VR environment. Experiments were repeated on three different frequency configurations of pattern-reversal checkerboard stimuli arranged in a 3 × 3 matrix. When a user was staring at one of the nine visual stimuli, the column containing the target stimulus was first identified based on the user's horizontal eye movement direction (left, middle, or right) classified using horizontal EOG recorded from a pair of electrodes that can be readily incorporated with any existing VR-HMD systems. Note that the EOG can be recorded using the same amplifier for recording SSVEP, unlike the VOG system. Then, the target visual stimulus was identified among the three visual stimuli vertically arranged in the selected column using the extension of multivariate synchronization index (EMSI) algorithm, one of the widely used SSVEP detection algorithms. In our experiments with 20 participants wearing a commercial VR-HMD system, it was shown that both the accuracy and ITR of the proposed hybrid BCI were significantly increased compared to those of the traditional SSVEP-based BCI in VR environment.
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Affiliation(s)
- Jisoo Ha
- Department of HY-KIST Bio-Convergence, Hanyang University, Seoul, South Korea
| | - Seonghun Park
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Chang-Hwan Im
- Department of HY-KIST Bio-Convergence, Hanyang University, Seoul, South Korea
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
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