Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG

By T. Mullen, Christian Andreas Kothe, Y. Chi, A. Ojeda, Trevor Kerth, Scott Makeig, Tzyy-Ping Jung, Gert Cauwenberghs. IEEE Transactions on Biomedical Engineering, 2015

731 citations recorded by the source corpus.

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 directdirected 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 ERPbased classification was equivalent to ProxConn for cLORETA (0.74 ± 0.16) butsignificantlybetterforLCMV (0.82 ± 0.12). Conclusion: We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from highdensity 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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7ea961c9ac8fa9b7ea74044ac722ec7b92936392

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Citations in the corpus

The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data (2018, methodology): “The ASR approach was then carried out with EEGlab’s Clean_rawdata functions adopted from BCILAB (Kothe and Makeig, 2013; Mullen et al., 2015).”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, methodology): “Because the use of wet electrodes for collecting EEG data requires attaching sensors over the experimenter’s skin, which is not desirable in real-world applications, the development of dry sensors of EEG devices has been dramatically enhanced over the past several years [37].”
Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings (2020, methodology): “Our empirical results suggest using a cutoff parameter between 20 and 30 rather than the previously suggested and default values between 5 and 7 [26] where brain activities were excessively removed.”
Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal (2018, methodology): “The mathematical derivation and technical details of ASR are available in [8][9].”
A multimodal approach to estimating vigilance using EEG and forehead EOG (2016, background): “Several commercial EEG systems have emerged for increasing the usability in real-world applications (Grozea, Voinescu & Fazli 2011, Hairston, Whitaker, Ries, Vettel, Bradford, Kerick & McDowell 2014, Mullen, Kothe, Chi, Ojeda, Kerth, Makeig, Jung & Cauwenberghs 2015).”
Theta power reduction and theta–gamma coupling desynchronization are associated with working memory interference and anxiety symptoms in panic disorder: a retrospective study (2024, unknown): “Unlike magnetic resonance imaging (MRI), EEG enables real-time observation and analysis of the brain’s functional interactions, offering a dynamic view of neural activity [34].”