The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
A 9.2-g fully-flexible wireless ambulatory EEG monitoring and diagnostics headband with analog motion artifact detection and compensation.
Abstract
An 8-channel wearable wireless device for ambulatory surface EEG monitoring and analysis is presented. The entire multi-channel recording, quantization, and motion artifact removal circuitries are implemented on a 4-layer polyimide flexible substrate. The recording electrodes and active shielding are also integrated on the same substrate, yielding the smallest form factor compared to the state of the art. Thanks to the dry non-contact electrodes, the system is quickly mountable with minimal assistance required, making it an ideal ambulatory front- and temporal-lobe EEG monitoring device. The flexible main board is connected to a rechargeable battery on one end and to a 13 × 17 mm2rigid board on the other end. The mini rigid board hosts a low-power programmable FPGA and a BLE 5.0 transceiver, which add diagnostic capability and wireless connectivity features to the device, respectively. Design considerations for a wearable EEG monitoring and diagnostic device are discussed in details. The theory of the novel fully-analog method for motion artifact detection and removal is described and the detailed circuit implementation is presented. The device performance in terms of voltage gain (260 V/V), bandwidth (DC-300 Hz), motion artifact removal, and wireless communication throughput (up to 1 Mbps) is experimentally validated. The entire wearable solution with the battery weighs 9.2 grams.
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“The artifact is typically hundreds of millivolts in amplitude, and 10 to 100 times higher than the amplitude of the recorded neural signals (Dabbaghian et al., 2019; Lee and Je, 2020).”
From Advances in Neural Recording and Stimulation Integrated Circuits (2021) · cites this paper as Dabbaghian et al., 2019
Citations
Cites
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Shoeb, A. H. (2009) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 855
Learning Robust Features using Deep Learning for Automatic Seizure DetectionPreprint
Thodoroff, P., Pineau, J., & Lim, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.00220 · Cited by 229
A $160~\mu {\rm W}$ 8-Channel Active Electrode System for EEG Monitoring
Xu, J., Yazıcıoğlu, R. F., Grundlehner, B., Harpe, P. J. A., Makinwa, K. A. A., & Hoof, C. A. V. (2011) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2011.2170985 · Cited by 224
Altered mental status: Evaluation and etiology in the ED
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Thodoroff, P., Pineau, J., & Lim, A. (2016) · Cited by 129
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Cited by
Sorted by citations
Advances in Neural Recording and Stimulation Integrated Circuits
Li, J., Liu, X., Mao, W., Chen, T., & Yu, H. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.663204 · Cited by 21
Metadata
- Authors
- Dabbaghian, A., Yousefi, T., Fatmi, S. Z., Shafia, P., & Kassiri, H.
- Affiliations
- York University (CA)
- Venue
- IEEE Transactions on Biomedical Circuits and Systems
- Published
- 2019-12
- Volume
- 13
- Issue
- 6
- Pages
- 1141-1151
- ISSN
- 1932-4545 (ISSN-L)
- 1940-9990
- OpenAlex
- W2969463944
- Cited by
- 56
- References
- 25
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 9 · 2023: 15 · 2022: 7 · 2021: 6 · 2020: 7
- FWCI
- 1.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Biomedical Circuits and Systems (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- CMC Microsystems
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Analog and Mixed-Signal Circuit Design
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- ambulatory EEG, motion artifact detection, motion artifact reduction, analog circuit design, polyimide substrate, active shielding, low-power FPGA, low power design
- MeSH terms
- Wearable Electronic Devices, Amplifiers, Electronic, Electroencephalography, Equipment Design, Humans, Signal Processing, Computer-Assisted, Artifacts, Monitoring, Ambulatory, Diagnostic Equipment, Wireless Technology
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