Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG

October 22, 2015


In recent years, there have been significant advances in wearable, mobile, dry-electrode electroencephalography (EEG) systems. These are yielding exciting new possibilities for scientific research, clinical diagnostics and therapeutics, and brain-computer interfaces (BCI) outside the clinic or laboratory. However, these systems have been limited to a handful of channels mostly for applications of low-dimensional signal analysis in gaming and command control. Here we describe and evaluate the first high-resolution dry mobile BCI system supporting real-time artifact rejection, imaging of distributed cortical network dynamics, and inference of cognitive state with a 64-channel dry-electrode wireless EEG headset.

The HD-72 headset features a wearable 72-channel (64 EEG + 8 ExG) form factor, compact electronics with active shielding, and a wireless triggering system. Active dry-contact electrodes leverage a pressure-induced flexing mechanism to contact the scalp through hair. We observe comparable dry-electrode signal quality to simultaneously recorded wet electrodes for average evoked responses (AEP, SSVEP, P300 corr. > 0.9) and single trial data. The headset wirelessly streams the 64-channel EEG data for real-time analysis and data visualization. An open-source real-time software framework performs adaptive artifact rejection using Artifact Subspace Reconstruction (ASR), cortical source localization using anatomically constrained LORETA or Beamforming, power and multivariate Granger-causality estimation using sparse vector autoregression via ADMM, and cognitive state classification from time-frequency effective connectivity features using a novel constrained logistic regression method (ProxConn). Demonstration of the framework on dry EEG data showed substantial suppression of signal artifacts, while single-trial classification of Flanker-task response error commission was significantly above chance (AUC 0.74 ± 0.09, N = 9).

The real-time EEG analysis software pipelines are made freely available in the open-source Source Information Flow Toolbox (SIFT) and BCILAB MATLAB toolboxes for EEGLAB. Distributed data acquisition and synchronization are supported by the Lab Streaming Layer (LSL).



New Here? Sign Up

Looking for increased exposure in the field of biomedical engineering? EMBS offers journals, conferences and a community for biomedical engineers. Membership includes PULSE Magazine.