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Erickson B, Rich R, Shankar S, Kim B, Driscoll N, Mentzelopoulos G, Fernandez-Nuñez G, Vitale F, Medaglia JD. Evaluating and benchmarking the EEG signal quality of high-density, dry MXene-based electrode arrays against gelled Ag/AgCl electrodes. J Neural Eng 2024; 21:016005. [PMID: 38081060 PMCID: PMC10788783 DOI: 10.1088/1741-2552/ad141e] [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: 06/14/2023] [Revised: 09/17/2023] [Accepted: 12/11/2023] [Indexed: 01/13/2024]
Abstract
Objective.To evaluate the signal quality of dry MXene-based electrode arrays (also termed 'MXtrodes') for electroencephalographic (EEG) recordings where gelled Ag/AgCl electrodes are a standard.Approach.We placed 4 × 4 MXtrode arrays and gelled Ag/AgCl electrodes on different scalp locations. The scalp was cleaned with alcohol and rewetted with saline before application. We recorded from both electrode types simultaneously while participants performed a vigilance task.Main results.The root mean squared amplitude of MXtrodes was slightly higher than that of Ag/AgCl electrodes (.24-1.94 uV). Most MXtrode pairs had slightly lower broadband spectral coherence (.05 to .1 dB) and Delta- and Theta-band timeseries correlation (.05 to .1 units) compared to the Ag/AgCl pair (p< .001). However, the magnitude of correlation and coherence was high across both electrode types. Beta-band timeseries correlation and spectral coherence were higher between neighboring MXtrodes in the array (.81 to .84 units) than between any other pair (.70 to .75 units). This result suggests the close spacing of the nearest MXtrodes (3 mm) more densely sampled high spatial-frequency topographies. Event-related potentials were more similar between MXtrodes (ρ⩾ .95) than equally spaced Ag/AgCl electrodes (ρ⩽ .77,p< .001). Dry MXtrode impedance (x̄= 5.15 KΩ cm2) was higher and more variable than gelled Ag/AgCl electrodes (x̄= 1.21 KΩ cm2,p< .001). EEG was also recorded on the scalp across diverse hair types.Significance.Dry MXene-based electrodes record EEG at a quality comparable to conventional gelled Ag/AgCl while requiring minimal scalp preparation and no gel. MXtrodes can record independent signals at a spatial density four times higher than conventional electrodes, including through hair, thus opening novel opportunities for research and clinical applications that could benefit from dry and higher-density configurations.
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Affiliation(s)
- Brian Erickson
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Ryan Rich
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Sneha Shankar
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, United States of America
| | - Brian Kim
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Nicolette Driscoll
- Laboratory of Electronics Research, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
| | - Georgios Mentzelopoulos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, United States of America
| | - Guadalupe Fernandez-Nuñez
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Flavia Vitale
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - John D Medaglia
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Drexel University, Philadelphia, PA 19104, United States of America
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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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Cisotto G, Capuzzo M, Guglielmi AV, Zanella A. Feature stability and setup minimization for EEG-EMG-enabled monitoring systems. EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2022; 2022:103. [PMID: 36320592 PMCID: PMC9612609 DOI: 10.1186/s13634-022-00939-3] [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: 10/04/2021] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
Delivering health care at home emerged as a key advancement to reduce healthcare costs and infection risks, as during the SARS-Cov2 pandemic. In particular, in motor training applications, wearable and portable devices can be employed for movement recognition and monitoring of the associated brain signals. This is one of the contexts where it is essential to minimize the monitoring setup and the amount of data to collect, process, and share. In this paper, we address this challenge for a monitoring system that includes high-dimensional EEG and EMG data for the classification of a specific type of hand movement. We fuse EEG and EMG into the magnitude squared coherence (MSC) signal, from which we extracted features using different algorithms (one from the authors) to solve binary classification problems. Finally, we propose a mapping-and-aggregation strategy to increase the interpretability of the machine learning results. The proposed approach provides very low mis-classification errors ( < 0.1 ), with very few and stable MSC features ( < 10 % of the initial set of available features). Furthermore, we identified a common pattern across algorithms and classification problems, i.e., the activation of the centro-parietal brain areas and arm's muscles in 8-80 Hz frequency band, in line with previous literature. Thus, this study represents a step forward to the minimization of a reliable EEG-EMG setup to enable gesture recognition.
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Affiliation(s)
- Giulia Cisotto
- Department of Information Engineering, University of Padova, Via Gradenigo, 6, 35121 Padova, Italy
- Inter-University Consortium for Telecommunications (CNIT), Padova, Italy
- Department of Informatics, Systems and Communications, University of Milano-Bicocca, Viale Sarca, 336, 20126 Milano, Italy
| | - Martina Capuzzo
- Department of Information Engineering, University of Padova, Via Gradenigo, 6, 35121 Padova, Italy
- Human Inspired Technologies Research Center, University of Padova, Via Luzzatti, 4, 35121 Padova, Italy
| | - Anna Valeria Guglielmi
- Department of Information Engineering, University of Padova, Via Gradenigo, 6, 35121 Padova, Italy
| | - Andrea Zanella
- Department of Information Engineering, University of Padova, Via Gradenigo, 6, 35121 Padova, Italy
- Inter-University Consortium for Telecommunications (CNIT), Padova, Italy
- Human Inspired Technologies Research Center, University of Padova, Via Luzzatti, 4, 35121 Padova, Italy
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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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Faisal SN, Amjadipour M, Izzo K, Singer JA, Bendavid A, Lin CT, Iacopi F. Non-invasive on-skin sensors for brain machine interfaces with epitaxial graphene. J Neural Eng 2021; 18. [PMID: 34874291 DOI: 10.1088/1741-2552/ac4085] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 12/06/2021] [Indexed: 11/12/2022]
Abstract
Objective. Brain-machine interfaces are key components for the development of hands-free, brain-controlled devices. Electroencephalogram (EEG) electrodes are particularly attractive for harvesting the neural signals in a non-invasive fashion.Approach.Here, we explore the use of epitaxial graphene (EG) grown on silicon carbide on silicon for detecting the EEG signals with high sensitivity.Main results and significance.This dry and non-invasive approach exhibits a markedly improved skin contact impedance when benchmarked to commercial dry electrodes, as well as superior robustness, allowing prolonged and repeated use also in a highly saline environment. In addition, we report the newly observed phenomenon of surface conditioning of the EG electrodes. The prolonged contact of the EG with the skin electrolytes functionalize the grain boundaries of the graphene, leading to the formation of a thin surface film of water through physisorption and consequently reducing its contact impedance more than three-fold. This effect is primed in highly saline environments, and could be also further tailored as pre-conditioning to enhance the performance and reliability of the EG sensors.
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Affiliation(s)
- Shaikh Nayeem Faisal
- School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia
| | - Mojtaba Amjadipour
- School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia
| | - Kimi Izzo
- School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia
| | - James Aaron Singer
- School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia
| | - Avi Bendavid
- CSIRO Manufacturing, 36 Bradfield Road, Lindfield, NSW 2070, Australia
| | - Chin-Teng Lin
- Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Francesca Iacopi
- School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia.,Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems, University of Technology Sydney, Ultimo, NSW 2007, Australia
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