title: Real-Time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG.
abstract: OBJECTIVE
  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) .
  
  
  CONCLUSIONS
  We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG.
  
  
  CONCLUSIONS
  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.
authors.forename: Tim R
authors.initials: TR
authors.lastname: Mullen
authors.forename: Christian A E
authors.initials: CA
authors.lastname: Kothe
authors.forename: Yu Mike
authors.initials: YM
authors.lastname: Chi
authors.forename: Alejandro
authors.initials: A
authors.lastname: Ojeda
authors.forename: Trevor
authors.initials: T
authors.lastname: Kerth
authors.forename: Scott
authors.initials: S
authors.lastname: Makeig
authors.forename: Tzyy-Ping
authors.initials: TP
authors.lastname: Jung
authors.forename: Gert
authors.initials: G
authors.lastname: Cauwenberghs
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D003071:Cognition / Q000502:physiology*; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods; D006801:Humans; D008297:Male; D059906:Neuroimaging / Q000295:instrumentation* / Q000379:methods; D013647:Task Performance and Analysis; D055815:Young Adult