Miniaturized Wearable System for Multimodal EEG/ECG/EMG Sensing and Real-Time Physiological Monitoring.

Overview

1. School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
2. Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
3. Institute of Bast Fiber Crops, Center of Southern Economic Crops, Chinese Academy of Agricultural Sciences, Changsha 410205, China
4. State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai 200438, China

Abstract

Real-time physiological state awareness is central to next-generation wearable computing, yet most existing electrophysiological signal acquisition platforms remain limited to single-modality sensing, high component cost, or bulky form factors that hinder everyday deployment. Here, we present a compact, low-cost wearable platform for simultaneous electroencephalography (EEG), electromyography (EMG), and electrocardiography (ECG) acquisition. The system integrates an analog front-end, a microcontroller, and a Bluetooth wireless link on a compact single-board platform (5.6 × 3.8 cm, approximately 12.8 g with the selected lithium-polymer battery installed), with an estimated bill-of-materials cost of 67.40 USD. Experimental validation across three healthy subjects, with the ECG channel additionally benchmarked against a commercial clinical-grade ambulatory ECG recorder, demonstrates that the platform captures ECG waveforms with recognizable P-QRS-T morphology under controlled recording conditions, supports reliable R-peak detection and heart rate estimation, records stable resting-state EEG spectral features, and distinguishes EMG activation from resting baseline in both time-domain amplitude and time-frequency structure. Leveraging the real-time wireless data link between the wearable hardware and a PC-hosted MATLAB environment, we further explore application-oriented signal processing scenarios. As an offline algorithm-pipeline compatibility demonstration, a CNN-based seizure detection pipeline is applied to the Bonn EEG benchmark for five-class epileptic state classification, achieving 86.60% mean classification accuracy. The proposed system offers a scalable and affordable foundation for wearable human-state-aware interaction, with potential applications in clinical monitoring, rehabilitation, and brain–computer interfaces.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

The paper's code and data availability statement is in the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none (its code is available on request), so it has no map.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

The Bonn EEG benchmark dataset is publicly available at https://neurophysicsbonn.de/downloads (accessed on 2 June 2026). Platform hardware design files and signal processing scripts are available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 7 funders, 24 references.

Cite

This paper

Zhang, Y., Meng, X., Lu, C., He, Y., & Liang, X. (2026). Miniaturized Wearable System for Multimodal EEG/ECG/EMG Sensing and Real-Time Physiological Monitoring. Micromachines, 17(6), 697. https://doi.org/10.3390/mi17060697

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.3390/bios16070394

Hybrid Edge-Cloud Asymmetric Analytics for Portable Multimodal BCI Biosensors.

Journal: Biosensors

In common: other, EEG, 4 references

[2] doi:10.3390/s26103065 [code]

Subject-Wise Depression Screening from Eight-Channel Resting-State EEG Using Asymmetry-Aware Spectral Features and Connectivity Ablation.

Journal: Sensors (Basel, Switzerland)

In common: other, EEG, 3 references

[3] doi:10.3389/fnagi.2026.1885392

State sensitivity and five-year longitudinal stability of resting-state EEG biomarker candidates in healthy adults.

Journal: Frontiers in aging neuroscience

In common: EEG, 3 references

[4] doi:10.2196/84707 [code]

Wireless Electroencephalography in Research on Children With Developmental Disabilities: Scoping Review.

Journal: Journal of medical Internet research

In common: EEG, 2 references

[5] doi:10.3390/s26134045

Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.

Journal: Sensors (Basel, Switzerland)

In common: EEG, 2 references

[6] doi:10.1038/s41598-026-52672-8 [code]

Validation of portable, semi-dry electrode-based electroencephalography device for its application in brain-computer interface solutions.

Journal: Scientific reports

In common: EEG, 2 references

[7] doi:10.1038/s41598-026-56070-y [code]

SSDLabeler: realistic semi-synthetic data generation for multi-label artifact classification in EEG.

Journal: Scientific reports

In common: EEG, 2 references

[8] doi:10.1016/j.ynirp.2026.100356

Combining EEG signals from the 2 members of a team to improve event identification.

Journal: Neuroimage. Reports

In common: EEG, 2 references

[9] doi:10.3390/biomimetics11060377

PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification.

Journal: Biomimetics (Basel, Switzerland)

In common: EEG, 2 references

[10] doi:10.1007/s10548-026-01245-z

Pronounced Nonlinear Traits at Epileptic Seizure Onset and Offset.

Journal: Brain topography

In common: epilepsy, EEG, 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.