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The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Real-time neuroimaging and cognitive monitoring using wearable dry EEG

Open access
Mullen, Tim R., Kothe, Christian A. E., Chi, Yu Mike, Ojeda, Alejandro, Kerth, Trevor, Makeig, Scott, Jung, Tzyy-Ping, Cauwenberghs, Gert·2015·IEEE Transactions on Biomedical Engineering
DOI 10.1109/tbme.2015.2481482
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Abstract

GOAL: We present and evaluate a wearable high-density dry-electrode EEG system and an open-source software framework for online neuroimaging and state classification. METHODS: The system integrates a 64-channel dry EEG form factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then, we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in nine subjects using the dry EEG system. RESULTS: Simulations yielded high accuracy (AUC = 0.97 ± 0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity [short-time direct-directed transfer function (sdDTF)] was significantly above chance with similar performance (AUC) for cLORETA (0.74 ±0.09) and LCMV (0.72 ±0.08) source localization. Cortical ERP-based classification was equivalent to ProxConn for cLORETA (0.74 ±0.16) but significantly better for LCMV (0.82 ±0.12) . CONCLUSION: We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG. SIGNIFICANCE: This paper is the first validated application of these methods to 64-channel dry EEG. This study addresses a need for robust real-time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and brain-computer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes.

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Metadata

Authors
Mullen, T. R., Kothe, C. A. E., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., Jung, T.-P., & Cauwenberghs, G.
Venue
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2015-11
Volume
62
Issue
11
Pages
2553-2567
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
DOI
10.1109/tbme.2015.2481482
OpenAlex
W1936982107
Cited by
795
References
14
FWCI
15.86
Type
journal article
Language
en
Open access
green
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Funders
  • Swartz Foundation (Old Field, NY, USA)
  • Army Research Laboratory
  • Cooperative Agreement Number (W911NF-10-2-0022)
  • NIH (1R01MH084819-03)
  • NSF (EFRI-M3C 1137279)

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CoChat. (2026). Citation check: Real-time neuroimaging and cognitive monitoring using wearable dry EEG. https://app.cochat.ai/papers/10.1109/tbme.2015.2481482
The paper
Mullen, T. R., Kothe, C. A. E., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., Jung, T.-P., & Cauwenberghs, G. (2015). Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2015.2481482

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