Real-Time Modeling and 3D Visualization of Source Dynamics and Connectivity Using Wearable EEG

By T. Mullen, Christian Andreas Kothe, Y. Chi, A. Ojeda, Trevor Kerth, Scott Makeig, Gert Cauwenberghs, Tzyy-Ping Jung. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013

383 citations recorded by the source corpus.

This report summarizes our recent efforts to deliver real-time data extraction, preprocessing, artifact rejection, source reconstruction, multivariate dynamical system analysis (including spectral Granger causality) and 3D visualization as well as classification within the open-source SIFT and BCILAB toolboxes. We report the application of such a pipeline to simulated data and real EEG data obtained from a novel wearable high-density (64-channel) dry EEG system.

View Paper: https://www.semanticscholar.org/paper/7d9ad6967443db1363a90407d7c2346ad17a054b

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Citations in the corpus

The PREP pipeline: standardized preprocessing for large-scale EEG analysis (2015, methodology): “The implementation is based on functions in BCILAB (Kothe and Makeig, 2013).”
The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data (2018, background): “…et al., 2014, SASICA, Chaumon et al., 2015), but they have largely been developed and tested on healthy adult EEG data with low levels of artifact contamination (Nolan et al., 2010; Mognon et al., 2011; Mullen et al., 2013; Bigdely-Shamlo et al., 2015; Chaumon et al., 2015; Hatz et al., 2015).”
A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update (2018, background): “This can be measured using features such as spectral coherence, phase locking values or directed transfer functions, among many others [31, 79, 110, 167, 225, 240].”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, methodology): “proposed by [51], EEG data collected from a wearable, highdensity (64-channel), and dry EEG device are first reconstructed by a 3,751-vertex mesh, anatomically constrained low resolution electrical tomographic analysis (LORETA), 1650 IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,”
Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG (2015, unknown): “In this paper, we describe an adaptive spatial filtering approach called ASR, which we briefly introduced in [20].”
Causal role of cross-frequency coupling in distinct components of cognitive control (2021, methodology): “The data were then cleaned using an artifact subspace reconstruction algorithm to remove high-variance signal and reconstruct missing data (Mullen et al., 2013).”
Day‐to‐day individual alpha frequency variability measured by a mobile EEG device relates to anxiety (2023, methodology): “…preprocessing pipeline to include (1) lowpass filtering and down-sampling to 250 Hz, (2) automated artefact rejection using the Artifact Subspace Reconstruction method (Mullen et al., 2013) and (3) manual ICA rejection of eye movement artefacts, heartbeats, muscular artefacts and channel noise.”
Unified Coding of Spectral and Temporal Phonetic Cues: Electrophysiological Evidence for Abstract Phonological Features (2021, methodology): “Next, an artifact subspace reconstruction (ASR) algorithm adapted from the EEGLAB software (Mullen et al., 2013) was implemented to remove high-amplitude artifacts.”